{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 案例数据背景介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.数据来源：\n",
    "\n",
    "     Dongjin Cho，韩国蔚山国立科技学院城市与环境工程学院\n",
    "\n",
    "     Cheolhee Yoo，韩国蔚山国立科技学院城市与环境工程学院\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.摘要：\n",
    "\n",
    "    本案例所使用的数据集包含了韩国首尔2013至2017年夏天的14个天气预报（NWP）的气象预报数据、2个现场观测数据和5个地理辅助变量。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.数据集信息：\n",
    "\n",
    "    这些数据是为了修正韩国气象局在首尔运行的LDAPS(Local Data Assimilation and Prediction System)模式下次日最高和最低气温预报的偏差。该数据包括2013年至2017年的夏季数据。输入数据主要由LDAPS模型的次日预报数据、当前的现场最高和最低气温以及地理辅助变量组成。此数据有两个输出数据（即第二天的最高和最低气温）；2015年至2017年期间进行了后期验证。\n",
    "    关于LDAPS模式: http://qikan.camscma.cn/jamsweb/cn/article/doi/10.11898/1001-7313.20200103?viewType=HTML\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.数据属性介绍：\n",
    "\n",
    "      1. station - 气象站编号: 1 - 25 \n",
    "      \n",
    "      2. Date - 记录日期: yyyy-mm-dd ('2013-06-30' to '2017-08-30') \n",
    "      \n",
    "      3. Present_Tmax - 记录日期的0-21时最高气温(Â°C): 20 - 37.6 \n",
    "      \n",
    "      4. Present_Tmin - 记录日期的0-21时最低气温(Â°C): 11.3 - 29.9 \n",
    "      \n",
    "      5. LDAPS_RHmin - LDAPSM模式预测的次日最小相对湿度 (%): 19.8 - 98.5 \n",
    "      \n",
    "      6. LDAPS_RHmax - LDAPS模式预测的次日最大相对湿度 (%): 58.9 - 100 \n",
    "      \n",
    "      7. LDAPS_Tmax_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 17.6 - 38.5 \n",
    "      \n",
    "      8. LDAPS_Tmin_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 14.3 - 29.6 \n",
    "      \n",
    "      9. LDAPS_WS - LDAPS 模式预测的次日平均风速 (m/s): 2.9 to 21.9 \n",
    "      \n",
    "      10. LDAPS_LH - LDAPS模式预测的次日平均潜热通量 (W/m2): -13.6 - 213.4 \n",
    "      \n",
    "      11. LDAPS_CC1 - LDAPS模式预测的次日第一个6小时平均云量 (0-5 h) (%): 0 - 0.97 \n",
    "      \n",
    "      12. LDAPS_CC2 - LDAPS模式预测的次日第二个6小时平均云量 (0-5 h) (6-11 h) (%): 0 to 0.97 \n",
    "      \n",
    "      13. LDAPS_CC3 - LDAPS模式预测的次日第三个6小时平均云量 (0-5 h) (12-17 h) (%): 0 to 0.98 \n",
    "      \n",
    "      14. LDAPS_CC4 - LDAPS模式预测的次日第四个6小时平均云量 (0-5 h) (18-23 h) (%): 0 to 0.97 \n",
    "      \n",
    "      15. LDAPS_PPT1 - LDAPS模式预测的次日第一个6小时平均降水量 (0-5 h) (%): 0 to 23.7 \n",
    "      \n",
    "      16. LDAPS_PPT2 - LDAPS模式预测的次日第二个6小时平均降水量  (6-11 h) (%): 0 to 21.6 \n",
    "      \n",
    "      17. LDAPS_PPT3 - LDAPS模式预测的次日第三个6小时平均降水量 (12-17 h) (%): 0 to 15.8 \n",
    "      \n",
    "      18. LDAPS_PPT4 - LDAPS模式预测的次日第四个6小时平均降水量  (18-23 h) (%): 0 to 16.7 \n",
    "      \n",
    "      19. lat - 维度 (Â°): 37.456 - 37.645 \n",
    "      \n",
    "      20. lon - 经度 (Â°): 126.826 - 127.135 \n",
    "      \n",
    "      21. DEM - 高程 (m): 12.4 - 212.3 \n",
    "      \n",
    "      22. Slope - 坡度 (Â°): 0.1 - 5.2 \n",
    "      \n",
    "      23. Solar radiation - 太阳辐射量 (wh/m2): 4329.5 to 5992.9 \n",
    "      \n",
    "      24. Next_Tmax - 次日最高气温 (Â°C): 17.4 to 38.9 \n",
    "      \n",
    "      25. Next_Tmin - 次日最低气温 (Â°C): 11.3 to 29.8\n",
    "      \n",
    "\n"
   ]
  },
  {
   "attachments": {
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5Tl/jns5Yp5Vr/BNEpUagYrVAIBAIBLqDAGsFt4mCd8JLuWQMLqJOvDETn8o4Wh0hY7Dw9irfBfO8LKHeZL0VGzRk/DRoMfc3YmMpoWtBpgyweeAqv9V3h6jAkwbFfmeZddZi3nnmSZaB+eafLx1j3/32TRFArCsGaUJax2Y9aK8mDivHfPPNnyxStDjWRVg6a/Bn0UIwxjtd/tU63FAaAjlslmHJGiRqJzeDOBeTyKlyY/XikkP6uN8QiHnnnTcR17y95RljeUzGZrKdIREiNXxymPitt47tQ3n/iJPwa9odup32MvEil6KLcip/FpbyxM2Wm/7Jz2Kfhx9+eMIIEe9uC6LSXeRiu0AgEBjwCBg0mLtlDuXLN2iJamAyNzAQDjLNV5MQgwhLCFGpQdybLzP6hRdemISI3Bce7DQn3XkD7c8LgwzkUGHkgDuBm4N2wvlm14c+doeo2I4Ox3EM0jncFf5zzjVXcv1sssmmBfdStghwN7C4sC5VN+LizTbbvFhttdUSqeH2ya6U6nXr+VuOmeoGJ/1gzWiv5X7B8Kyzz07WpLweLYu+Ezdz6YnwyYn1rCMvC6LTUYK3vJ9G/Ayi0ohXJfoUCAQC/Y6AwcAg5gH/1FNPJWEpc/oBBxyQit8x3WdLSHWEjKgZBMVArUgcS4jcHwSFdBAsIdw99A6t1IQy54ghWWENvLnRclTXeekuUcn7LH+6Xu0N/uV1muE7i1G58GAz9Lm3+xhEpbcRjv0HAr2MgMRQeRBs71ASNfHje8Pqj0ZPINU5gZ8y8cImpU1nheivZiBgCcm5QggImalFu8ikyccuKoRYU+6PbFLnZ8/fWQdU7ZXC/KyzzkpEhlmcVsCbfquRkM6uFQuHwnaw4W7hSqil1ZOo1HK8WKc5EQii0pzXLXodCFQQuPnmm9MAIY14e83gawBBFvqjGfjz4F7+NOiPT7TYnf56i/dWisBxoRCnOneRIdwxCAZrh5weLCF50jd9EhJKGChXCBKCWCkQRyQo1FPCMuvyz0crklYDlggd10NXNDNBVOIOqgWBICq1oBTrBAINjEDOE7Hc8u0nUiKME3rYXgZKPvyyeb58mpZlH395vu+2Ia7rbFCyjL5i2222SQP7mDHbF0iViAThjAgCwahQz3JzzM87KCNvmaypzkktEomlJCyj78i6EO4WoZD2jXhktwyXBJ0Ea4nKumqSyDPCGgUf+gX7Hl+uEKLBu+66K4ULl/vN9ZA1BOX55e/63xHe5fWa4Xu1FSXnKelK34OodAWtgbtuEJWBe+3jzFsEgRxSKWNoe030heJlNALcHSJNCD4PPviQVMeDJaZcb0WEirwTs882e7IuCI/MgyvyQXswfPjwFC4rIkXmTPO5OwgW/RY6Ka8HUpJrquRMmbmPCy+8cCIwOe+F4mgGLqG4Cy+ySLKAEJeyCMl2ad+SkiEcwnQRkbKFxndRJnme6q8SgeUU5XKJ5KgLfVDLhOhTP/VFTZW/f/L31D0RNRtvvEmKzBBSKqJC5IXGCgRP62gEi7aVP0JdlJ122ql4772xok2DObyFZRJIWi5EtLvZWNMBG+CPayk6hRVF6G93Bb9BVBrgYjZBF/qNqBBVucH5hWNqHQz46vMDvQnu/5bo4viIitwaBu8rrrwiWS/yQL7CiiumAddvmUmRGBEUiIsolT322KMYNXp02tZAqxmkrb/yyisn3csqq6ySfiMm3C2WIREzzThjGvwffuihVIjMfGGKOVeIgY67wPxNN92skByKJsTv6knfslVELhG1Y9QfsR4xKw0OQuK3fBHm7bjTjsWvfvVEgWRw9XDhsKbYF5eNgRVx+cY3vp5ylmSM9t5rr0S6ROvYn/M95uhj0nfnTHcy+ktMZGxlRVljjbF5QszPIaKIj4gdZCafz0orrVhsvNHY/BtIVnshoI1+Q+ozl5hzEjLdU51REJVGv+KN0b9+Iyq77bZbeoB52yOyi6n5MaD4Z1L3Fhyt7xAYH1GhzTCwIBMiWAz6EkNlFwfrgOVyfHjT951rRlQKdxHNBlIhk6X8D6wkOT13JgjcKMJFM/kwiDmu+jHIgn06blmYah5rR96/36xCLA9cMuYb0IW3yithea5LQhjsWMiGJi+J5UstvVQiyohIzp9hG33PLjL3qPOSZ0MiLUJaxdOEdL74wotpXWTH/oS1vvvOu4ncyJ2hZdHos88+l/KZWM8+czvowAPTtgTDiJnz1s+c5CxrXG677ba8SVN80vnIXSK02gtJd60o5ZMNolJGI753hEC/ERXm4PyP31HnYn7zISCjJs1AtL5DoKtExcDKHZEbt4V5LARcQr4jCZJg+WSFMDhxD7GECrVFKMo1YyaddNK0nm1N1ueGMUAb3MyjDZHb4phjjqlkKx05cmTx2T8/S64T63CNcCuxSEh2xr3grV3+B8SibK3LidJYNU488cR0DNZZjVaEC8k2s88+WxLP2jfNCg0Ly4jEVlxJjotAjR41qnjkkbFkBGHTD8tMclvAWcukiSbD4G15OfNpvh7IHmJoOWtMbtkVJt9FMzRWlEyu1lhjjeQyq1e/EZVmwaFe5xz76ToCild2pdRC3aonE77lYkdd73Zs0agIKEKGrETrOwTywLjyKqu0e1ADpsGS8JT+xPdRozZI69KW7LHH7mmeyrA5QocYdY7ZZ0+Ew/rVE/JCk5HdNVwlsloiBvPOO19yxyAVn3z6aXLr2F6IdG5Eq9NOOziVrv/0k08rRIMolruEfsSLjD6zhowaNSr1IRdfM3giQKw1ZaKSI5vMUzxtookmLlguXnrxpWQNsr392qf+0ZAQ5bII6aP043KnsLCw2jz/3HMpkiW7n6zP9WRdBMog63s5QRcCZJ4IGBYV37nIcuNSM49lqtHbOeeckyxXXIGsS1mrVK9+y5jqmsMypsCgo3tAtB4LZa0tiEqtSA3Q9YKo9P2Fl5HSwEe/4Q3V5K2dIPbuu+9Oxc4sp8MgjPWdNYR7RhruSSb5SpRaFqiyPpisj5DQa+Qy9KwrXD05NTqLBisFy8Riiy3eBgQJz+zjjC+tHXkhLYz5cpmIpOEikYODq0fulRWWX75gcSmKL9JDyrrOUfE221qfxQcpyZYgETy5EfXahj6FZYN7WTZYAy6iY5kifVwyiAkLkOXWnX666VKlYS4iYllVh63/7rvvFdttN/Z8nnzyiaTrYfUZPO20xV0/+1lyH7H06Bs3WT4Oa1Ju3N721ciWBCQtp1wfMWJEuj65//X8lMmX65G2J6bAoKN7YOONN65Uv67l/guiUgtKA3idICp9d/ENsEJ7s9vD4FfrxEKAFEhYpqqu7VhEDK5cdwgB0sFCMs+881TctFwr5tOWiJbhRrEtYvTRRx+lZVwuLDW5qVFjneyWyfOz1oN2RjNoGfTzOcw8ZEibEvfeqAz4SMOJJ56Ujq+fHREVwmBkzf702ScXUg7T5oZS22XaaadJbivkjV5FY/3JdWqcK7wU+9PoUeyLpkdjseHiyv2edvC0FZ0WPM0vE5Xcp0YlKsgeQTQrChzqoUVJQMWfQKCPEAii0kdAN+thgqj0/MoZ5LlGuA0kGSMG5U5gIkcsvOGycAjTzUXUEAqDPJGsiBhaApE0RGgGWIOi8GRvylwrkqHlJqzYPAN+bkKFuWZNolfKTdZRbguWD1oQ23Kj2J5lhEWinJWVtcI6jlNu0sibn6vBWua43DOmcjr0B3/xi+Kss36Ujmk9rptB00xTLLTQgum4H338UdpXeZt8LH266aabkiC23C/LET3YENrqZ7n5Le+L5Swjucm3ot+inHJzXEnh9Lt8PjLgWpf1KTdEyTzn0EgNFtlCtvbaa/eaFaWRzjn60poIBFFpzetat7MKojJ+KA38BnbCVAMoVwT3xJgxYxIJISRlKSBIzW/piAjBaq4fwxpBYMYdww1DCKuGjBDjHM0z/p40zxpwggUytuOOOyaxrd/n/zT0UPW4iu5BViOWFLqUaIFAMyMQRKWZr14f9H0gExUmcm/Z0qwz+T/44IOFkG0iVdVxJSzjFskJyDIJ8YmEEIZaLiLH+scee2xKT+5NXf0Yrp56ixn74Jao2yEefujhQnZbxQARtRyqXLcDDMAdua9yGDc9UNlyNADhiFNuEQQagqgQDzJnl02v8PVmKWtm2T+ecTeIeIPNKcF9GkyY1imNmbGFG5p8N7322mu9NjDoK01ALlGe+zm+T2/Lxx1/XEr7Pb51q5fDhTvBw8m5mXz3Fk5f4DfXABOwyXdCx48//qh6Vx3+bmWiAntmf64PJn4RJtwxIhc22GCDlFCMJcRbKTFlJiLeVJEQWhBJwUS8uX+JO4ld3X8sLOWS7B0CHAsCgTohwIoij4z7lWtxIJPgOkEau2kQBPqNqPzv//1f8nHz6x5zzFg1/0/O/0l6yzTvt799M+VdEMUgBFOIYdkHLE038zlhX64XQuHvTdYb7owzzFBMN93gtA/5Gww01PuZSPA1yzdBCe/Nw9txR5PlfL0yOHqr1vjG+a7zYCQRlggLWTKF8zK3Eq6VJ6I2NUXKDxDf5XQwCPJzawceeFDSJci8ufjiS6QB0/dll10uDYzw0PjGuRWEdTpnGgffvZ0Kr+RqIKCbeqqpiimmmDJ9J0Lklqi1NStRQUKQNW+UrgkNhnPhXkFCCEfdF+6XTEB8uoZwdK8Qp0rJjoAiMRKIIXqIYJRtr/UOivV6GwEvJ6x27l/PKi8k0QKBVkKg34jK3T/7WSq5Tl2v9Lp/simmmDwliBIyOfPMQ9I8gy1BocySBIW5SJiBiLXFdkgE0R6/PiLhDVfeCFEFl156acqlYFDK0QguILO7fU455VTJVIqk+CdvbxLymFOLe2vRDFRIgrolGuW/t2xZXBEo/WpvQjiyhci5IE4IBwy4CViVjj76mJRQykC56qqrJSIjugEZQbhYUTQCSsRk9IYbpsFY1MJ8889XLL7EEslKkEWFBl01XAgJJb6qtWS7YzQiUXHtkTUajvvuuy8lDpLjQrguMikiQ04Q2JSvAezcW3B2ndWbkQYegZS7hDgVsaE5KQtRE9jxJxBoMAS8LPn/FHLOyuelKFog0IoI9BtR8WZqkBdnLTkTC4CBRDl2g8dEE30t5VY44fjjU2p+SZn8I+ZBPl+MU045JeVeEJmQ21NPPZXqgchEaVDyBu145SadOLM+i0wtjUXHoJeFaQZLA+H22++QNkdUvIlzsXAniArIE2JhQhYkrcrhgTJ6moeseOOXOEq/Omr7fHufRILsXxPFIKGVQXmLzTdPYZZCMpGqjz/6KNVL2XWXXZMpeIYZZyjke5CzIodzdnSc8vy+JiosTCIuYJcjZKRfl0tENk16BsSVidv9komI74SZiK+cHCwh7ivWLUnG7Mt5I6gZ//J5xvdAoJkQ4Nb18uL+9yIlcV20QKBVEeg3ogJQAwfLh4FE81Yg6kFjATGoakzv5UyQaWbpj8FHU4uEbmCZZZdpkwcBGZBp0wDGDaAhBAZ5IZ+aUEvagqxr8em3UEiDJzdPNVFBCogkNURl6qmn6pQEOL6CanmgpIWwz3KoY9pZB3/GEpWJEhGyCqLCQuDcCDyJNWeZdZYUaWJgtm/WlO3HbF9I0sUCZJ7kW7W2ehIVVgo4esjqA4sPEoKk6h8LEtIF13KiMt9ZnITwWsc1U5NGHg/aIJlJEVH7riaytZ5nrBcINAMCnh3+J7l01UaiRYkWCLQ6Av1KVIC77jrrpoH1zx9/XEw11dQpI6X5iAqCwnogUVN2ueQLQifgLcJyOSo0Wg/bsbwgPAY4GfAuvvjiZE0wSHN/aJmosOho/vnpNyb55iRJt8Ccyu3EPcSt5Bi2L1tUxiUqU4+TuyHt/Ms/1USFi8oxEA5kiH6CG0teDZPcB8Sajz32WNqD9bmVskWF60c/JelSvfb883+arA3Kzj/3/HPJ6kDcefpppyc8kAPnmPdX7ltH3+FSS1FC/WcZgu4LYgoAACAASURBVBWSJGmWRGP0MDQz+iTrKPdVtWtM7hBWEoRLVst99903XY877rgjpW5nLYN/kJCOrlLMHwgIeGnKejbPhbIVeSCcf5zjwEWgX4gKbcZuu+9WrL/eesWCCy5QDBs2NL0pIwKzzjJLJUkRkiAtuMGNmXPkyHUqRGPNtdZKg66BepVVVk1XUP0QAx7hI/0KomKeDJkGefvP1ptqoiIVOXeCwZHOhd6Du+lrE02UrCTtEpXpv7KocNsgVEiHdUV9+G4S3vr7998fx/WTiQrLEkEwtwUiYVA3aCu6ps+5eNM+e+9dTFQiKixJ+qhAGpeSicXh2OOOLV5++ZX01iXBF/JDbMfygBh1laggZ/DyYFRHBUZcKkceeWSx0047pf1zs7HuVJMQ7jGWEC49pDEnLHNNRHupAcMSwpUWLRAIBMZFAEFXTRqh594MK8q4GMWc1kagX4gKISoNATeIQdTbAesBISQ9CVKy8cabJJJhvsgcgzEhah60DXLEsosvsXhyH7lMUmGL4uDq4fIwaBo8pejOKbENslo1UeE2YSEpNw8HxAGRGB9R4cbhnjDoKhCHJHFlmQzWU04xRSIdXFjZ9VNNVBATxCo3VglEhQBW23uffdoQFVoclhTT5ZdfnrDxPYtokThht7QdJuSrq0TFQ5GVip4HkdOfPPltmQgauRuIWblj9JewWfQBPKrDzvP5xWcgEAh0jgAin4X8noO1uok732ssDQSaC4F+ISpliO6+557kmuFeyNNF6ftF6beB95JLL6m4O8rb+i5yg0tBQ3qQGdaMp59+OpGWAw44IEXHyHFhgM2l3WshKkq9d0ZUEBDH1xABRIP7w+DuuFxX3zv4e0mDQT9jUGdZ6IyofGujb6X9+cNt04ao7D2WqOR06VxFdDYzzjRjxZKhT47jeN6+uLoIUPfcc8+Ud8b+sk6ncqBOvoiIYd0hxD3iiCPSuQmxhi8XVITpdgJeLAoEuokAKwrST4vi/7gW92s3DxWbBQINj0C/ExVuAdYHb+Xzzz9/1bRAspYYXLka2muElQS52mabbZYiQVhRcigvq4LB3ABuPzn7ZXtExXIJv7iNWFO4nczjuvmfv/xP+p41KgZolhuiTo3+hPujs8als8Tii3dIVFh9ykQlV9GtWFSqNCrcSvp6zz33Fttus20ibKKLCINZNLjOzjrrrPSg8+ntbNCgaZL7prN+lpcRu+bjl+fH90AgEOgdBCSuzFYUVuawovQOzrHX5kGg34mKJG3+GblX/EOWp/fefS+9/SMLmSBUQytaZK4550qzuR+4fLzxcxF5G7GcJoO41n4UK9OqiYr1kSVRM3Quprnmmjs9MAz8pnI/5DDgDuLWQA64nLh/OmtLLrlkm6ifatePY3IVcf+w0OQquNndZf2ymFafEDKi1RNOPCERElYcpETjdnH+tjn00EOTfiVrdDrrZ3kZouL6RBKpMirxPRCoPwKsKPIBCbXnRhYEEC0QCASKot+JyoILLZS0D6wiLArlScG2IUPGJn4jkG2viSjJFhWRMtwUGosHt81xxx2bEr0RciIa6qxomaiU86hw29guTwSeOcGcBGNlopJ28uUfuVwsy2Xiy8vK35NFZYlxLSosNtxBQpxF8GRhrHOR5I51RJNLpExUJDxzXAJVuWeI7bjB5FW45+57kg7I8kO/f2hy+3AJIYZdCU/OBM++aYAkWIsWCAQC9UVAEsYVVlgh/T9vueWWlaSO9T1K7C0QaE4E+p2oICMGY6JP+pHydMeddybfrMGW1aC6XXvddWlwtvzGG28svv3tfYu9996nshrNiv2dcMIJicCIUskNUUGCuItE3CAk3mjamyyTsdRxzq2qRCp/B2vKaqutnkKM5V7xJkTomycWEdNss82a9CLVGhUWmVqavDITTDBh0uBY/7JLL0t9UtuDZYX7KTfaEqSEziY3ZAtZOuec2jNYsmQ5LksNXzkM6GyIdyVmixYIBALdR8CzhRWFyN3/sGi4aIFAINAWgX4nKjQqknzttddeyaLAqpCnPffYI2WONTgaeMst6zdYSo76wVHJesIaIRvpRRddWFx62WWJ3BDiIgwErgZX1W8RBdYT1gcPCNqYeeaZp8OJOwmhqu6HfCHzzT9/0tjkOj1jycRXkTG2KU/Lr7BCRaPinFlIOiMqHmSOw33FdWR9WXIRKll35VVwTnQ+SMyYMWOKo48+OpEzWCBwNCZXX311+o60EeYhZ7U0eVTgp8mRIkcNbY5zov3hUpLALVogEAh0DQFWFM8s/0usKIIAogUCgcC4CPQ7UUEApJ4XhsyykpKdrTE24Zkw5RWWXz79I1dbVAzy6gHRiWjeRIQ78+0atA3o+ZPPlxvIA2HllVZKlg8EgAZkoYUWTq4TUTEdTbQh8pUQ5Ioe0mhUuJ1kh2QNyo3AVdiwqJg8eSDlhxK3jORoWi2ZaZESJCqTnR133DFtS5/C5ZX1KypJExYjft7M9JXepZxmPu/DeWerTu53R5+ISrWQGUkSOu2a5X06NpcSXKIFAoFAxwjI0HziiSem/x3u2Pwi0PEWsSQQGNgI9DtR8abfWQIyb/5ClKtr9SAooljKDQFAFKxLOf/CCy+klPgsEs8992wiEPmtxbosAbkScXk/HX13vEyMrCM8t7pfHW1rvmPJqJubBGwIWC4BkOdXf0pAx2qiem/ZEsISU64obTvLWT64qmhYuKKcfyZNErZZVmtrj6iUt7V/VaiRTaRFKDQ9UXW/ytvE90BgoCLghYLr1P8KoXvOMj1Q8YjzDgRqQaDfiUotnYx1+g+B8RGV3LOPPvoo6WG4yTyEJYijCYrQyoxQfA5kBAj0uV39b0gs6QUtWiAQCNSGQBCV2nAasGvVSlQyQFxqsv/KoumhzOXG/x7RQhmh+OxvBLhm6beOOuqoFC3nO1JNw5YtgaycymqIekMyqhsXJ9E8PdhHH35UvbjN78cffzxZGv0/0JCx+kYLBAKB2hEIolI7VgNyza4SlTJI3GL0PWogeUjTENHU5MGgvG58DwT6CgGRcO7H9qY11lgzpS6gASMWX3CBBTq8X3Oeo2oXdD4PBOeYY45JxxFhqNJ3tEAgEOg6AkFUuo7ZgNqiJ0QlA0WXYz/5wS6xnbfYiBbKCMVnXyLw4x//OJEHEXO0Wywst916W0ryiLzccMMNqTvPPfdc0rqVheeSKJq0nD32lZdfqXRf2oM//OGPqQCoAqH2J1eTXEm5sajkfeR58RkIBAIdIxBEpWNsYklRJIJRHfXTXWA88CXcGzlyZHqAi0iSx6YrtYe6e+zYLhDICGSiss3W2+RZ6dO9iFgI3xeZs+aaaxQbbbRRquzN1XPcsccl0bi8RWp8Scw40cQTFa++8mra/uKLLk6pDrKw3L6sm2tzPfnkkykcWcZsUXlyE5XF8W06Ez8CgUCggkAQlQoU8aU9BOphUWlvv95W5ZwhLPRAFwkhuks4drRAoDcRyESFRQRJfvSRR1JUHfeM6a233ir++te/phIcROFICreN+1TOJaJYaQb8nmzyyVMknyjDcioAiRXzb5E99il1ArG54+fUBAp9RgsEAoHOEQii0jk+A35pbxGVDKxwb8n8chI5xEXl5zfffDOvEp+BQF0RyEQF0aiecp0sIf4sI5JREoivt+66aV1uIk15DcvkaqJRYXmxr3nnnTflVWIpYXExj9uHWNd3JOf1119PrqEpp5wq5Twqpzyo64nGzgKBFkGgbkRFsrSu1JBpEfxa/jS4faqT7fXGSXMLKZg4evTo9ECXsI9vX7RQTpDXG8eNfQ48BDJRWXqZZZKl5Jprr02aKaRDwkT5jpSHQFRYQPxGpCVQLAvBR22wQYpqm2222dI9i4g88vAjFUDVxjIP6ZYw0vdhw4alLNcSXc4551wpu3TO7VTZML4EAoFAGwTqRlT23XffZL73DykEL6bWwGD48OGV7Ldt7pxe/PHqq68WBx10UPLje7jrgyRyf/vb33rxqLHrgYJAJirC5ssti2OV51BiY9ovLSqid9Zee2Ry5eQIH4QlWwEXWWSRVJncvcoCqRGQK7A64QQTpHDkAw48IBEVma2FPnMtebG75557KoVPy32J74FAIPAVAnUjKvywd999dzJ7SikfU2tgcP/996eH6le3TN99Y34/88wzi4UXXjg95HMSud/85jd914k4UsshgEwgFax35UYga75kbMgJC4oINe3SSy9Ny0Ty0FJlt448QfQpInkGDRobhr/++usnSwkhLcvge+++l6KLfJe5WZ4hxT4JapWeCIth+SrE90BgXATqRlTG3XXMCQTqg4AIDCQ4J5HzwN9kk00KpQWiBQJdRUCBUjW6pLAvN6TYfNWMaVC4dBT+9F2TEwiRMQ0ZOqRYbPjwYoYZZqiUpHjxhRdSQdW11lor1R7bYost0ro0KRpBbrKyTDhhmq+WWVfKWaSdxJ9AYAAiEERlAF70Zj5l0UL77LNPEiEaMLyhKkgZgsRmvqp923fEQ3RZWW+iBwix+dwyX/z3v4Wkb7kOl3pkInncc3vssUfKg8LqonSEIp2Wm3/XXXelk6G5IqwdNGhQ0rvkM+RSUh+LezNaIBAI1IZAEJXacIq1GgwBAkfRQjmJHLeQvBT57bXBuhvdaVIEEA5FNxEUQltum/YawexMM8+U1hNqP8ccc6Tv9cpB1N4xY14gMFAQCKIyUK50C5/nbbfdVtAFGEzkrmDSV2naIBMtEOguAlyLXD/uK3lPRAJ11mSb5TYSrbbffvule7Cz9WNZIBAI1IZAEJXacIq1mgABERlysOQkcgSPrC5M+dECgVoR4BISdYagCCMWGBAtEAgE+g+BICr9h30cuZcQ8OYrsiNHCyEuP/jBD4qIFuolwFtot7/85S9T9lkkZffddw/tUwtd2ziV5kUgiErzXrvo+XgQkB3U27AIIQPPxBNPnGoLyV0RIaHjAW+ALWZF2X///dN9Ms888xR33nnnAEMgTjcQaFwEgqg07rWJntURASJbtYVywbjFF1+8uOKKK1JirjoeJnbVhAjQoiywwAKJpIjcicSCTXgRo8stjUAQlZa+vHFy1QgQPNKtICqsLKKFdtl1l+Lll1+uXjV+tzgCtEvZiiKUWK6eaIFAINB4CARRabxrEj3qAwS4ftb9stAcwmLaeOONiwceeCBVy+2DLsQh+hEBNaSEG7vuyn+EFaU+F0M26SeeeCKVB1AiIKbAoL174Jlnnik+/fTTmm+6ICo1QxUrthoCwkgNVAcccEBx9NFHV6KFll566VRbiPXFP9QZZ5yRxLhXXXVVJUmYlOlcRx7Mr7zySnH66aenfzz/fJdeellxxBFHFCrxRhXoxrprJAZETFx37p6wotT3+vhfGjlyZMpp5HtMgUH1PSDflfxXt9xyS803XxCVmqGKFVsNAUXpDFhqt2gykXILqZhrvnTqE3994iTCnXvuudM8tV5kML355pvTb1aYqaceW+Pllw89VIwYMaL4+te/nrKSqgOj3otIkmj9jwArypxzzpmumzwn1Zlp+7+Hzd+DPffcs7j22mub/0TiDHoVAS8LXvRqbUFUakUq1ms5BDJRUU233BCR66+/vlhpxRWLGWecMaXr33yLLYphw4alQe6DDz5ILiJkxrT7brult4PLL7ss/V5jxIjiH//4R/HCCy8Uo0aPKs4777zy7uN7HyOAgJatKKojR+sdBJS3YHmMFgh0hsC3v/3tLt0nQVQ6QzOWtTQCHRGVfNL/+fd/kvtmkkkmSQQkExPF61TY9btcgffXv/51IjbmcysoYhfJwjKa/fMJ/6xFkQo/rCi9ex0QlSuvvLJ3DxJ7b3oEgqg0/SWME+grBDJRufDCC8c5JK3JhhtumNw/3DvHH398oYZLJiuZvPC/ltt7771XnHTSScUaa6yRXEbW//73v19eJb73AQKKCxo04b/QQgsVP//5z/vgqHGIICpxD9SCQBCVWlCKdQKBoigyUfnud79bPP3008VTTz1VPPHkk8WLL76YBjaD3IjVV69gtdpqq6WB75RTTkmWFMsRFoRG9VwaiE033bT41a9+lbbxaR2hr5FgrgJjr39ROHC22WZL2B9yyCFhRel1xL86QBCVr7CIbx0jEESlY2xiSSDQBoFcyBCZKE/f+MY3UmbSGWaYofCdC2fjjcdmt7Ue7YkkYb4TZ2aBZk4mN8sss6QoopwRVwRQtN5H4KOPPkrXynVRPuH+++/v/YPGEdogEESlDRzxowMEgqh0AEzMDgSqEaA657qRsVYUSJ7yWzgysvnmmxdrr712qop7+eWXp/VfffXVQqZbycIeePDB4rPPPiu4j5ZZZpk2hEfl3euuu676sPG7FxBQQXuOOeZI+CtM+cknn/TCUWKX40MgiMr4EIrlEAiiEvdBINCPCKgjxKUktHmKKaYopGT/xS9+UfznP//px1617qFZUYTEsqIsssgiCevWPdvGP7O+Jiq/+93vknVTXa9aGnesF5AvvviiltXHWee3v/1t8fDDD3foyuVC7q0s19zH//73v9OzxPPE9+xSFmXI9eylqbMmAq6j9u6776a0C31RbT6ISkdXIeYHAn2IAKvLoYcemkKbDaISHJ199tmFys7R6oPATTfdVMw666yJpHCvhRWlPrj2ZC+9TVQQEmQhX+tjjz22+NrXvlZIzqhZ7ntHLwarjxhRzD777JXltGUsqayj5cRkrKx+E8eX2w477FCwlHbULKNZq25PPvlkeoHZeuutC4kmTb5vt92YVMJjzJjt0++8LC3faqu03muvvZZ2x1LIautZIinlsssuW3gx0vbaa6/0f/DTn/60ePzxx1N2YKRMYsrcHnrooYJbmsauvXbJJZekfTz/3PPtLa7rvCAqdYUzdhYI9AwBbzDnnHNOerghLNNNN12KAsoPn57tfWBubSDabbfd0kPVwBAJ9RrnPuhtomLwZanMLtUf/ODodB9komLgVr+LZaO9tu666yVxe14m2SPr56BBg1LBUjozU/79/PNjB21lAS677LJi9tlnS1FkktrZ1uBfbgsuuGAxatSo8qz0/fbbb0//+9NPP31B+2bSzykmnyL1/5vf/Gb6nZf5zOs6Z23FFVdMkYRSIiy51JJpuwfuv7+47fbb03e45GmaQYPSPFlgc3vjjTeKlVdZOc0//PDDi88//zxNrDGsM5JdekZJeY/wmf/Pf/6zYrXJ+6nHZxCVeqAY+wgE6oyANzwhsptttll6GHggfOtb34oU7l3E+cYbb2xjRfEwjdY4CPQ2UVHqYuKJJy64KbRjjjmmmHDCCSsWFYn9Jp10ssrvjMyjjz6aciLJb4QEyIWEZPzrX/9K63744YcFN2Ke8u9smckWC/+3BPY5PcGmm25WXHLJxYk477TTTonksFrssvMuxS677FL85LyfpC5IIsmloqZUnqRAyBmukR6/87LyZ+7DCiusUMiMrSnPMWTI0IJFSZ9MJ598cuEFiOtpgw02SPPuuuuuDEH65PLaaKONkkXGeksttVTKM7TgAgtWckDpv+zc8g/NO9+8xbPPPttmH/X4EUSlHijGPgKBXkTAw8QDddppp00PE5WciXHVoYnWPgLemA0EHshLLLFECgdvf82Y258I9CZRYQEwiK600kqVU0RUvjbhhMmlygrASqGMRXUzMLp3kJo8sO++++7Vq3X4G4EhsudmUqNGckcTjQw3jH0qs4HETDbZZMU8886T5rGCdNYeeOD+tN4111zT2WppGaIyYvWx57bcsssWO+64Y7Heeuul81Vg1fHvvPPOVJuMZeWwww5rs0/kK7uyPGvUKdtiiy1SqgW5opZYYmxFeWkYkBmWG5/Os94tiEq9EY39BQK9hIAHx/nnn18sueRYM650/XK6KHIY7SsEbrjhhkr5gqOOOqpLVVe/2kt86wsEepOocKEiBJNOOmkl+y2LAvLBYsHaZjlrhwi9clP2glUF0TGg05AR1e68884F3Ylp1113TeHtCDESMGbMmPT/yNKhsYDSiCi5QUOSBbnDhy+WKrGzzkguKO0B0mTAX2655crdGOc7K6s+s17Ql3QmhkVU7JsrCCH61a8er+yP1UNdMfsysZRkUpJXYuGZdtrBScOS55U/kSXb9gYxKR/H9yAq1YjE70CgwRHwwFN/ppzXReI4pums6m/wU+iV7nnjM4B4eA4fPrzDB2yvHDx22i0EeouoSAeg+CdrCbLqnlAe4Yc//GEx6WSTFs8991wxzzzzpPuEsNpyolGEIbeXXnopuY1YMt96663i7bffLmaeeUgxdOjQRITpRGxnOZH2zDPPnIjJp5/+I1lOWEq8WBDJs1iw8Gh0Uht9a6PKd4RGU0W6VqKSczAhOkTi7TWWpPXWXTcRKC4s5SC4e1ZeaeXUH/q3rbbaKpEm5zLllFMmwpKL/yFq008/Q2FZ1r2Uj6NGk/Pvrail8rGCqJTRiO+BQJMh4GFK3e8h6aHhzUhRQ9aXgdSIFQ0gMDAwEfVF618Efv/736fyEEhBR603iAqywb3CUvLySy+lQxOscgEShdKc0K5wy7CSaKwiqpiXrQqsle4nE8uK/zUWDNoRnwZ9ywhxzSOEz9aUHBHjBYIFlFVnu+22K2699dZERr610VhyglBzl2iIisiczhrxr2NefPFFyaWUq7Svs846KQy6TLS4kcw/7NBDk9j3hRdeTJafdddbt/jp+T9N/aV5QQKeeeaZpGPhErr00ksrXRB9xCrkxYj72f64jzbZdJOKCwu5gq/5zuHU006tbF+vL0FU6oVk7CcQ6EcEiOkuuOCClBvEg8wbFwJDud/K7Q9/+EMyqztnJE1Zg2iNgQCdBpcDAmAAMwDmaJvcw94gKqyKam2JnMmNNYGL9LTTTkt9Iq5V+qLckJZskWSRQf5ZQlhAEB8ROq+8/Eqx+ojVU6SL8+FGsl+i9+OOO66yOxYIBIXIVJTQRBNNlIj0qaeeWqy77jrFdNNPlwZ9+xetgyCwzKy55pqVfbT3JRMVxFzzf682mPvfJAt2blw/9vfZPz9LFh/5g37zm98kKwq3lSm/4Gy//fZJ4NtZ5faRI9dJpSaQo2mnmSYdb7ZZZ03nOP/886fCqgjdQQcdlLtQt88gKnWDMnYUCPQ/AsIG77777pQh1xujyVsdsze/fCs1D2sPWoMFkWRn/vrePm9uJ0Tx5ptvSqGbvX28Zti/e5FrIg+iPocNG1YYFOlD6EAQFS6E3mg0HNwurCgsKFw7omD0g7WERYTbx/IjjzwyWRX0g4tm7ZEjC6J1pIRFQa6RH/zgB8laaXtEJFtNvAwgGix62b1TPh+DLNFu/v9bYYUVi8GDBycrBHcLC49cKlxVtRKV6orTyj8gQfkYjo+orLLKKqkrzpWrB3mTOXullVdKImMkCZFExJZYcsl0juW++05wSxhMU5PbTjvvnMjXq1/q4xCm3kz8FkQlIx+fgUCLIfDmm2+mB4yHooerQcPDu9mTyLGi5AKRHrDM1v3dvI3DWB2nf3wppuzvPvX28RERidREskiEJlfJGWeckQSmtCGsCUKD4dLeJGSW1e/qq6+ue1f1K7t/aEUmm3yyZB0xKOsLVwwxaVo22WTJTWQw11TSNoBffMnFifArEpobl8cMM0yfhLE/+cl5aV9cXKwr9iunSLmx0Oyx5x4VC6cBl/VBXS9t0eGLJtGt7wjE+DQq2aJSTVTKx8zfE1FZdSxRefCBB1L/lI6QKyVHDcmEPeNMM+VNxvmkh0PYhgwZUtHvSAoHP1YkzX2AYBED91YLotJbyMZ+A4EGQYAJnkm3HC1kgOBzb7bm7dvbabailN/y+vNcJJEzUBmcPx1PrhZuiI76bYDtSF8jB0zWQLR3rvVMtiUXh7dkZJc1gbZCFI37htZirbXWSqZ+rgtujUxEXBcWAu4SYm945GXlT2/3dFQGzVoG3fbOt7N5BliaEZYu7ibT2OMdnO4dAlC/8zLkN+fYse3111+frhHdxbzzzpMORb9C97LTjjum3zJHO6d33nknZb/1/dhjj0nLkK+VV165GDbLsETW4MJikjPVbrLJxmk9OOXMtDURlXvvTcesBbNVVlm1WHyxxRJ5pFfRPwSFOHbLLbZMWWpdx0FTDypuuvmmQg4VROajkr4N8bJd2a1FqzLBhBOkSKh0EkVRnHjiiWm93iCdjhFEJSMdn4FAiyPg7e6BBx4o5EDw8DF5EAt59HBu5OatNSe/IzhsBCtKGa9MVERXdERUWLMIOoWVs7xIxY4MaESLBnauLO6RMWO2qwg7RZwYWMy3fPSoUW30FawZRI4InIHv9NNPr2gtyn0sf/cWLDeGfSMiLALM+0JsvR2zItBMeHPO94pPgxwLHRGo68FtIvwWkSGaNWgjCDnpmCib8vb5O2uK1hsalfJ5Vn+HjTwqWYtSvbz6t4Ec2bJ+zsvz6y9T1CP/zofuw3L6jNVXXy39L5177rnpxcCgzkrimltPG77YVwLasph2HVE/y3YentwViwq3D4Iiv4nji0BynRBLlqVsXXIOiJTJd8+D3BRZ5T4Wnq09+9yziXgpF5Ab4k1QPNlkkyfrS1nQm9fp6WcQlZ4iGNsHAk2IgDdKojcmbg8n1hYPMtaXRmve0vTTg9SbXW88CHt6zmWikt/My/ukp4GzgUnqcZEefhs4//2ff6dQWedn8DP4W+ZNO5vV/aaxMPm+/PLLp90TT7JqGIgOPPDAFI5ruQc7EiLEVF6ZH/3oR8kaImEXokcQKe27dfPEWsAaoo8ylcqjIYJKFlRRH3Jv0JVkElI+v46+51wm+Rg+3WtZy9FbRMWATLMBX9MZZ5yeCBWrhT7QrMAkL/dJaMuNVW7Wn3+B+dM9h+Bvt+226bv/E9Yl+8qDuOXcOtUWMXoYuOb5XDLTzzB9cl8iASwt8qwQwK+xxhrlw4/z/Z57x0b91GJRki2zLgAAIABJREFUWX6F5YvVVl0tabfy/wwLEsE5YuF6itZxXJYT8xFmxENDPJEaLzMal0+2yhLiyh3jt3tvooknqrj5kKl6tyAq9UY09hcINBEC3qoNRAqXeeh6aCmw1hdJnMYHk0GREFi/5IToLMx1fPvq7eVticrYhF/lY4owISDlijDQi0pxXjQCRMCTTz5FikYh0GSVQDBy3RhWGloPgynyYVBhwmdV8rZsP0Si6hmJfPLbVH5j9huhYSmhJTAwEpJKdEZ8bV8GIq4n5KgejZWGVcYgbQDO/RISm1tvERUWpny88icyyCpUnlf+TnRebu47ViwWE+QK2TAQZ5LHipLJvUihbDUp7wPh++akk1YySdN8uJ4IIy2P6+E70jK+zLR33nlX6rv7ZHwN4UQkOmssaHQ67TUCY9jktPoEu37r+3TTT59cf3Dmvrv2umsLy1ng3Itf/Pe/7e2y2/OCqHQbutgwEGgdBDyIPaS96XtgeiAhCd6k6zVwdQUtBd3oGDwUDer90Yeu9LcNUWknhwsXzwknnFCsvvrqyRWAfMB4n2/vkw5z8cWXJBJhHkIhnPeqq65MERfVgy5RNHdEWRvC4iRbKV3ELLPOkvbNVQFH7j4DqLfpvsAR0UKEnIv+CN+lyZltttnS77KbsbeICkLonGls3nzrzeKtt95M3xE905t5evOt4s00vZl0Jtnqka89cihyyP9HbtyQSJ5IMxaI8TUWLfvI+/7hD09Kovbq7bjDWMw6ayyhsOWuG19jfaQd6awhPCpBl6OF8vqIq75nawx91CMPP5zqA3308cftuou99LAelq9x3l9PPoOo9AS92DYQaEEE1BbifkAUDDaLLLJIElL2ZvhhhlH2TxERjutttpwXIq/TiJ8PP/xw6jMS0V7zVu2cDMzeULkZ/F5s+GJJG6KOTLZqmV89cQ+wSmy+xRaVt3nuC2/j1r3jjjsqh11jzbHWi9/+9reVeX31hdg5uxNF0ZQHQNqHam1RbxGVvjrfOE7fIBBEpW9wjqMEAk2HgDBmkQ1M1QZDxEXhsuyTr/cJGeS4npjnTzrppDZvsfU+Vr33x6Ki31NNNXUSXXLp8OOL8hAlk60fiAx3SCYitinXXKFfoE2xXKIwIb/WJ6RlmfAmy6RvOdHqdddel75Lw841RmdkGVdCfoOv97m2tz9WBufp2PQztSbeC6LSHpoxrxqBICrViMTvQCAQaIMAdwH/MyGmgdVkMJUIqh6NqyCLS7lGGlWLQr9Bh8CVQsyITMn2qe9ElAbp9iZ+exEYrCKWc62tvfbI9MmfzyVz+mmnp2ga+SqGDhmSrBI5S+iNN96UInokBxNxQ2NBP6DRu8hMykWWj80ShdT0VSN2FnHk+NxbWShby/GDqNSCUqwTRCXugUAgEKgZAW4h0SUEkgYm1hZ+6SworHlHX65IcEmYSPjJn17WAnR1X91dnz+dhgRh4moilrzwwgtTlk4uGy6XOeaYI/WzTAh8RxxoQ7hgaEK4zES6cO8Q0LI+ISIa7Lho5K9xzNdffy0Vusv9ltZdtMott9xS+F5u9mFbmgFkqbq99eZbKeuoUOWyu6V6vXr+lnskF4GUXK1WK0q5D0FUymjE944QCKLSETIxPxAIBDpEQPbOiy++OAlDERa6BNFCtWpK6CeyFUXkQ3XdlQ4P3I0F3vCJKwkRkRD5TOg76EKkPudKEb1RjgZhNWIJURVXJIYqswgacaVqtaJykBAEra+IQTdOvdc24ZJi/XHtiTa7W74giEqvXaKW2nG/ERUPD+GHsv1RF8fU/Bi4lt78upJnoaX+mwboyXALySFhcDdw+U442VGECSuKyBYWCaLSnjQkgTWECNcbPXcUnYbkZVxVok5YQ8qWEH2kGWEVUmiONWTbbbdN1hD1elhDuE64euodvdCTc22Ebf1/58RnQl/VvOlJC6LSE/QGzrb9RlT4MgnAsugsV3OMz7FVLZsRB/56oZT10i4MnH/D1jhTFgauj+wWEsUi5FKqcq1sRWHJqEWUK6yVGwQJ4fqQLE2op8J2knFlcWrZGoKIcMnIGUIbgoQIJ6X5kMPD4CpslVWoIzLVGlekvmchDwxRL3wV6OuuFaXcqyAqZTTie0cI9BtRMRDzx3r7FqcdU/NjQF8gZTYffbSBi4DMlqwmdAsGNRYNLyTcKASlWSQKIQnnWEPko6DNEGUkk6eXGNYOLqVqEuK3+QsvvHCqOYO0GDhFDSE0SIj9hjWkPvcg91YOr5aiv6dWlHKv9t5773Tdy/PieyBQjYBnQi3ZePN2E+QvPf10g5ZrCvR0f7F9YyBgoGF6jxYICG+WFAtZyRMLCK0H95Aw1qxzyMu5j4TjIiGsc9J0s9LQlbCoPPHEEynNObdMuBh7/x5jgZp99tnT9XMd2isP0JNeIECy66q6zA0YU2BQfQ+4NxBkLyK1troSld6oCVDricR6vYOA+hlBVHoH20baKwuoN23ZP0WaEFfKIItYCF2WD0ThPfVjMgkhTjXl3yJ95N6QPVUad9YQ+ThkwAwS0r9Xm0VKSn7XCml0jXujueYssF5wYgoM2rsHFL3k8vVsqLUFUakVqQG6XhCV5r/wSAISot6PjKtIiGgXlpAtt9wyRcFw4wgrzonMDGjcOvKJyPeRyQgtiugggltRN0J0/Rby+41vfCORGWHJQoOjNQYCt99+e8WKQg+EOEYLBJoJgSAqzXS1+qGvQVT6AfQuHpIIkpBUZEvOGaJKLtGpkvRM/dwvueZPJh20IZYJ1xVRw2/MeiYBGqEs0SxCI9MqDUlnljX6kYceeqhSdNAx1l9//URoung6sXqdEJDpNltRuOiQ1GiBQDMiEESlGa9aH/Y5iEofgt3OoZAQOUMkFbvvvvuSX5fAWXQFgSoiwhpSXVmXSwYJsdx61rcdAZv9iNBBbkThtNdE5tCdIBw0B7VE9OT9IEyyqw4dOjRtL4kcd4DziNY3CIjUm3vuuRP+quGGFaVvcI+j9A4CQVR6B9eW2WsQld69lOq38NVyo9B1CP8Vuid5GpKhQq0cJWWXDMsIN41lwnV33nnnlAX28ssvTySEiwcp6O7gxK+sVDw9Sjmip6tIEN/qU64tpO7Pfvvtl3QwXd1XrF8bAkTJSCmCyW0XVpTacIu1GhuBICoNdn2I3pjdGyUfRBCV7t8gBg1J82R3vffee1OIL/3GXnvtldwiqhiLkimH6yIkitchIQSsm2++ecoQq1y8cuuPPPJI0oW4T+rdWE24awxy6623XjpOPY7BLcQltdlmm1UStW266abFz372sy7VkalHX1p5H4iuXDOu3/77758S57Xy+ca5DRwEgqiM51ozuVMpV0/eNPnsmbSrlwm5zO22225Lhc5kfxRBseNOO6ZP6b7VVKluzPMSbHVFEV29j3r+DqLSMZqyMXOReGu94oorkmsFCTHIL7HEEolslCvpGkBMqhZbzqVifSREzRf74TahDenIJdNxb3q2xHVmQWFJae++7Nnev9r6jTfeSOnucxI5uVXOPPPMgvUlWvcQ+Otf/1qxoiAqhM7RAoFWQqAlicpbb72VyIO32FqaAcebans+dGLCPMDU+nnw975XOSwzvu08mPmM55lnnorv3oCmybKpVokm0ZXcE9VFzNLCfvgzEImKKBlJziQue/rpp9Obv1TsKtwinPKBECdyv1SnckdCmNy5ZLbeeuuCPoA7RxZQBNZ1te9GaYiRjLDuUdYUUTx90RATRD8nkYMbMW9v1gjqi/Pq62MgJbnSs9pMjXRv9TUWcbzWRaAliYpEMh68a621Vk1XThEu65977rnjrE9w+PzzzyfzPRO+iYlcgjvb3HTjjUlfkJcpaV8mPHzyk08xReFNkg5BngFvQBLebLDBBul43ir58TV98WbLZdAIrRWJCiLCakHL8eCDD6bw2kxChN9yyShzX20NoRXhqjG4cskgIfBhDUFCcqSMnCSN3rhjWHKIbp0XItYfTfZj0UKKBPp/MrkGslz3R+Xl/sCgO8eUqM2zBV7zzTdf0iZ1Zz+xTSDQDAi0JFG55tpr0j+wN8SOmrdlpd/HjBlTCds86+yzOlp9nPknn3xyOob03p01D5NpBg1Kpnxv2AiKRn+Q+7fsssumYmvmB1HpDM3xL0MShGWyqkkNbsCTXOiwww4rttlmm2KllVZKVi05QvLA6FMOEG/1OY27+8I23voVtXv22WfbENDx96Rx10CYkXjnzf0Eq0Zoks0dfPDBxYwzzpj65n+FizXXFmqEPjZCH0RtLbDAAgkjL0xefKIFAq2MQEsSFaJDD+FssWjvAp5z7jlpnfJgRWtSa8tWGKbzzhqiMvWgqZNJdpNNNklvPyrEiugIotIZcu0vy9YQlisp2BEJhAKxWHPNNZM1hJutHCXjGrMacLtxySAswmdtS0P0+OOPp8GaQLUZrCHtIzP+uawo0lkLZUbULrnkkvFv1A9rcAuVawvJASOShV5sIDdunYMOOig9t2QJJp6NFggMBAQGLFFR3twAxRWz3npjIx06Iiqyeq666qop+VW+KTJRGd/baCIqUw9KREXUgzehICoZxbaf5TTurCE33XRTcq14OEtItswyyyStTzlKBgkhAOWS4bKhHxGuK+EZUSjXDqsXKwvcB2qj/RgxYkQa5EaNGpUwaQYsWA/kgaHbcq1ZgGjPGiUqrq8wpKHjIoYBAfbf/va3vjp0HCcQ6HcEBixRKSO/8y67pAdAR0RFBI439L323quymTBTDw3kw4NDFI+onuqKkGOJyliLSudEZZW071Z2/Xi4crnRcxCXivag82DhQARFLMiAWraGcMnIv4HgEX0qeoaEKGonvPWZZ55J+4wHd+XWbPPFgM5NSffEiiLdfTM2Il+i81z0ECmlD2p1t5A8O0TGnjXE+BK5RQsEBhoCQVSKothh++07JSoffPBBMdXUUxf7H7B/5f4QRuzhMe3gwUl4OeMMM6aBwEOl3BCVQV+6fggw5+/QotK4RIXGY3xNBtVcT+aXv/xlcf311xennnpqCs1m5Rg+fHhFewC3PLGGyKwqXJcrbM8990wiT9t7i0Rs7Dta1xFgReHqgrUEco0i0O76mXy1hdw0SGpOIod8+Z8jjG61JlydTsf1Q9AjhLvVrnCcT60IBFGplahM1ZaoZNfPo48+mh4gf/nzn1NK8uqy6WOJyqC0jsJttCm0AgS0zaJREREihNtbLRJCAyRixLlJ3KVWjHTtSEfZGuItftiwYcViiy2WcosgISxRqutKACZSirXK4BOtfghwcZ100kkpdJpAWHbYVmu0SkJz1TPK5QO4tGiOmt3F5xmieCCCIvEfLVa0QGAgIxBEpSiK7XeowaIy1VTF/vsfULlXMlERqdBZ22OPPYqvf+Mb6W2W6XbTTTZJq8t9wN2h0V6svPJK6Xsjun4QE64aeUOQD7lDiFOJHD1IkS+EBXHhzhEl4w2XDojpOlrfIYD8IY4GObV6WsGKMj70fve73xWHHHJIsmw6b/9brC4sfM3WaLNEnjmPXXfdteiNDMTNhkn0NxBoaaLC3F1L274W108iKl+5fjJREerZWZO5lgaDqHPiib9eSexGN8DHrkketsSSS6TvjUpUkA+p3YkZRY4QOZbzxXSGQSzrfQRoUbIVZfDgwclq1ftHbawjEEwTUAv9N9CzJkklL0Ks0Rv35uGHH16xohCSRwsEAoGxCLQkUbnmmrF5VGaZZZaUmE2ugTx5S7n55pvbXP/tttsuPSAUY2uvCQvkCz/44EMqi7k+PAzffvt3lXmdfckJ4lgnRDH84he/qKwu4ZUIJO34449vuIRv+sz8zIoiQRgXD9HrkksumdxXHrC33HJLyuKKvDS76b1yYZrki4GYVcv9SAc1EKwo47s0XLKwUMARLnRSP//5zxsyWujJJ5+saG522GGHprQEje96xPJAoCcItCRRMbB6OHU07b3P3m0w68iiQsgpYZi3NC4PeVlYFugrciZNVhFKfAN1nvjJvd1p3pSQD32R64NLRJ4P+xO1Ut1EwXi4NlIKfRoVtWe4uYSGio464IAD0sNfpA5LS8ZaNtc55pgjEZh99923QP7ke2B5isic6qvds9+uCc2P+wVxVG8oWlsE/L/JszN06NB0jxJ1K2lAIN/fzbNByQz/O/oXVpT+viJx/EZFoCWJikyN3jLbm2QY/f0Hv29zPd55552UebTap332WWdVBuA8ENf6KWyW+FR6a9sQ0krHrxnwZ5555mKSb06SwiulXh++6KLFUkstVXloNUrNDkSss6gfosY//OEPKRmXB61BYcstt0w5H1hfMl7yYDDFi2IggDz22GOT7uX111+PzJpt7sbafijrQJANX3g3ShHL2nrf92t9+umnSVSc/8emnHLKZGUdn+u2t3oqfxOLpOvHikLPFS0QCATaR6AliUr7p9r1uUR63EQsJKwm3B++m3w37/bbb6/Mu/3225IFxjIPxnfefbdYbfXVU96Pf/yjrahU9AzhqSRnLA3S6yMzcq3Yf6M0REUG1642dVqEUyKGMOQqk4hN0jHhyDmBlwc1t5pwU5oiREfGVK4xFq2ICGqLvPsFyYObekRhRWmLTy2/RAuxiOYItQ033DD9P/dFVmJWsHz95IRh/Y0WCAQCnSMQRKVzfHq8VChyM7fuEpXOzhmJo6OQJ8VAi5yoPTPXXHMlDYxB2CTslElcfR5WmDPOOCOFpLJIDcTcKhLlZYsAYtsI7ovOrnOjL5NVWjFKlj73mxILqqVny2e9+68Sd64WzQ0cYvR6Ixz7a1UEgqi06pWt03n1BlHprGu0OQZkuVayG0kyOLqeLIz0qXAd14dEWCeccEKqYMzd9v777zekYLKzcx7fMiHeztFgCoerr756fJvE8i4gwPInnFmaABgLw1e2Qah3PRpLjYgsVsS4fvVANPYx0BAIojLQrngXz7eviUp73eNGojtSAPKBBx4ozj333KQvYIXx4De45Imwly5o5MiRxXe+850k/BVVJdV6M0Yj0TJkKwrRd1hR2rtD6jOP9dO9Ip1Avp/WXXfd5OLtrmWU6zOHS9svIh0tEAgEuoZAEJWu4TXg1m4EotIZ6KwNrDCikYh+5c1Yb731Um4a4dR5wPE2KzmdekFqM8lvI+qKmFJSLaLgRmpcW0cffXTqPy1DWFH69uoQwh988MGVaCFJ2M4777wkHK+lJ/La5DIbEZFVC2KxTiDQMQJBVDrGJpYURUpK1x0xbX+Dx3oiGkl0jKy66g7ttNNOyTpBhJoJDDeSDLvS/Muua3C66qqrChlCbd8fjXaHu0sfFbrsr370x7k32jG5hRQazdl+uYXkRHJfddRYUeiqXD9aoshr0xFSMT8QqA2BICq14TRg12p0i0p3LgxrBUuKqC1vvXQuq6++erK4SGaXSQyLDDeSGjJqr8inI1rrzTff7JWcMGq8HHPMMen4rCg33HBDd04vtuklBGRj3mKLLQoVvd0jEjfKqySSR0OOZW0WTYT80llFCwQCgZ4jEESl5xi29B5akah0dMEQBTl1WDS4WmTcFS4udFoiu0xgfMp67C1b9Aahq2rPL730UrcJjEq5rDr2vdtuu0VESEcXqQHmv/HGG4VSF+4B14uoW0kMCSFZ6FjuworSABcqutAyCNSVqHjYRmstBOg+Okv41lpn2/HZyHXz6quvJrfQUUcdldKzS9hVdiMZtGQcRmxyUjtJ8EQjScjWXp4OGpucndTAh/BEax4E6FZyNJrrT8tC3xItEAgE6odA3YiKZF7Ef0zjknXF1BoYcIt4GEdrHwFCXGna1ZEh0FVaQKSIMgJlK4ykdqpnqzlD8Csctqx9UG5gIOaGaR/V5pjruqtQjaCI6KFdmWaaaVIYMkE3bVQzRpo1B/rRy4GEQN2Iiocun61/2JhaAwNCQK4PIbLRuo6AhF6KTfrfQGC4BrgJssbBAJcn4ss999wz1aGhe2C9US+q0aKRuo5C620hXF69K9dRKv6LL764cpKSyHEZyvPj2tI4WTdS5Fcgii+BQJcRqBtR6fKRY4NAYAAiwPIybNiwNIh5AxdRtc8++xSrrbZau6UFRP94az/kkEPSgMhSyY0keyqXkflCYRul6RuL0XHHH198/vnnjdKtuvUDgVTqAglxXdTpaq/J+0NMm3OosLS4ztyA0QKBQKBrCARR6RpesXYg0C0ECHVFDhnguIDUg6puXD9CkWm9VPg1sIlGUhupHI2ktEBO+25/asfYxtt8f9dGYn3TJ2G8/d2Xanx7+ts1gT0rCtddrY04W+h71rLI5SPirJEIZq3nEusFAv2BQBCV/kA9jjmgEJBNd5FFFkkDOPeOLLldaXQOCmQ+9thjyY1EzOttfuKJJ077zAUeDYSsNdxLIk9EoghxfuKJJ5PAs7oiN3Gv6tW0FkS91U1NpRdffLHdSCbbWi5KqtzkEEFUhg0bmgpzlpeVv6tULkW9fXCl5Ea0bNJk4c1VobnA9FNk1SeffpJX75NPGZHpjpyXUHVRP91pzpUVLEcLLbTQQqm2UGQb7g6asc1AQiCIykC62nGufYqA4otqxhjgDE4y4darEfEOHjw45eyQlRcZ8savKrCU+2WLi+ObFN2jI0N0CKTVtmGpMRk077777tQ9lpk1RoxIwlDL9F3ZgtxUH1500UWTaJROY/To0RXCwrXhWMNmGdYuUWE1klRPttZvfvOb6dirrrpq8ZvXX0+7p+Oh11Ec0LG5xxAULhR5bRxPhuG+ytTLcqKfk046aXHBBRdkCHr0ydKkGGcOR2eh+fa3v510ST3acWwcCLQoAkFUWvTCxmn1LwKSgxnMDdpS9rMg1LN5C5dUTHKx9t7IWVJYWNRDIvbca++9Ul/0Z6qpp6p89ztP6iSJXBoxYkSad8QRRyTygjjMO++8yeoiPwi3junMM8+suLNoaZAQFg/7Y9lB1KqbAdpylglC48OPODz9ZmnSckZepIRu5/LLLy823HDDtM6FF16YctwsNnyxRJ56U6DK+pGtKMgTstQbDekTyi6sPeNy6623VpLI9cYxY5+BQLMhEESl2a5Y9LehEaBFOfDAA9OgM/vssxf33HNPr/SXSyQTFZlyq5u3dq4VYk/kQoIyA+Eqq6ySdDA5xbt5csHoK3KB+JhnGjp0aKqNtOmmm6TEdjfffHOycFh20YUXVg6JZJhHi/H++++l7x0RFeSCe4jVhgvlgAMPSOvTcGi5XzK85oZs2T9rUMpL8/TTxdtvv91rob+sR0LLkYeyJSn3pzc+nY+sxOVoIckWWc6iBQIDHYEgKgP9DojzrxsCrCgLLrhgGlQJYUXm9FYrExX6leomNJrbZP75509JyLh5DPasFJpt5D7iRjEgs6YIRyeG9ZYvr4tltmlvYi3aaKONiuOPPz4JRa1z2mmnpxo4vs8266zVXUq/Dcg77LBDstAMX2x4sdhii6f9S02vLbfccul3mXwpOrnHHntU3Fn6ykqVtSztHqgbM+l1svWG4LU/ErepLcQClgkbMqoKeEQLdeOCxiYtg0AQlZa5lHEi/YUAkSpiYoCea665UiXn3u5LmagY/KubwU1/vKULEyYI9ZtFhRAWGeF6UadGAUbZdC1/8MEHk4CWdYTAlpg2WzQOPfSwVBfJeiwwBlNaE7/zlH/TXbCKCMfmRsn1cLbffoe0LksFgazj2XbzzTdPp4BQEQezuGif/+/nSX+T+vrZZ4lILbvMsmmbetZCoj+hiUHaZGIuC3yrse2r3855++23r4im11xzzYJbKKKF+uoKxHEaBYEgKo1yJaIfTYkAIesCCyyQBk6CSPkz+qK9//77hWy3BnnWjeWXXz6JY2k8TjrppJQ8zjLJx5544olkLfEbuUBcJCLz+8Ybbyiee+65SlTSiy++VNFmGLC5j3LekKeffia5jehHhEwTmqpLZD+imriX1l9//fTbvDwJ6SXIXXrppZMg1nyWmCeffLLYcsst03o+tRVXXCkRFeeniXgaPnx4Woe+RdViUU328eijj6Z1evIHISpbUWhsGq1xkdEL5WghWHML5YioRutv9CcQqDcCQVTqjWjsb0AgwIrCPWLAFE0j6qYvG9fOoosukvQlrBc0FSYkAnlgxTCgi1gZMnRIsd122yVygqiwqNCJiKSZfPLJUl4QFpKcYTULSe3PvmlYkJCcJZeVBDnKLiPC2OyqMajOPNNMaRvb0VxwX3DXcCVxRfmeSUz+zAnRrE8EzJqQE8ZxU9Gn5P3JQ3PuOV9FIXUXd+JcomDHIwz+4osvururPtnOPXfttdcmUgo318a1RjQbpUlIKKoLQT7ssMNiCgzavQfcH+1Zgju6j4OodIRMzA8EOkCAFQU5MVggK3QFfd0MqlwzxLvVU3azsEaIVslv3giKSJyyW4PYlhWhvQgdDxIWlfYEnfaBnGTLRz5//aruT/7NZWFCPCS8Y5H5/ve/n6wqXD4IDPKTyQtiI+Gd8gOikYRfK0eQ3UL5mF39dF5KQzgOzQ63WLM1hEAoes6lIzLp9ttvr7jY+ut81AYTrn722Wen6+WaxRQYlO8B9war4FVXXVXzbRpEpWaoYsWBjgBCkiNcWAZYFqLVBwGkSkQQTcxtt92W8qgQ+3JpDRkypEJeWIxYXWhqiHm/973vFZdeemlyA8nqO752ySWXJCsKdxT3SbPrPZAuOqSMEZce119vhm53hjEBt+sXLRDoDIHvfve76aWjs3XKy4KolNGI74FABwhIhpatKISq7VkgOtg0ZvcQAZYjETiuwamnnlrsvvvuKdeLCtUEsNkCg8QQM9PJIDAEsqq5s+CwLHF/Wdcbv3mt1NyPXHdZv8NtRzPFItaXjaic1StaINAZAu7NsKh0hlAsCwS6gAC3R7aiyN7K5B6tMRCQYE4Kf3qWa665pjjyyCMLYc4ExdmNJKOsLL0sKEgKMmM9bpKO3FoaAjpsAAAgAElEQVSNcXbd74WILSHg+ZxFbck/w2rV2y2ISm8j3Br7D6LSGtcxzqIBELjrrrsKb+0GODqJViuy1wAQ92oXJNvLLhGuonXWWSeJeaXhz1YYYmOp7IVHq8Nz3XXXpSgpSfKy1qdXO9mLO+cWogGSuM/5yi784x//uCDE7q0WRKW3kG2t/QZRaa3rGWfTDwj8+c9/TgnGPNwXXnjhPo/o6YdTbrlDIhzEuKJ6uIsy6aAzktiNvkj4NV/5euutlypas7ZkApPdSCNHjkyJ80QFqdX06quvNp3bT8i80G45dJyfsHYJ855++um6X/cgKnWHtCV3GESlJS9rnFRfIcCKIkeIB7qCgiJWojUPAiKcttlmm3T96DW6ErrL0iBsW8FD114kDXefgT1XqHZfzDDDDEnMu+OOO6awZhFMIodkIm50ca7cM9xj2S2knhHtTw4F7+mVDqLSUwQHxvZBVAbGdY6zrDMCoiSINA1EcoQQYUZrLgQQDLlWXEMJ5YRj97QhHgpKIjw5GmnXXXdNYdMsNmUCg9BwI4lGkktEnhb5dQh36WkaqcmVc+yxx1Zcm0o/sDxxefWkBVHpCXoDZ9sgKgPnWseZ1gkBwspsRTHAiDKJ1jwICEvOVhT5WFhF+qKxQjj2Qw89lNxI+++/f4pGkjxP1A3CZEJmZp555pThV80iOWEQYTlsJHHrz4ZAib4QBq6voqiEGHc3Wqg/iYrcPkjhyy+/nDIYy2Lc3mR5zi00PuyR3RtvvDGVnBjfuuXl//v550nXpvp3fzSJF7n3+up/oavnGESlq4jF+gMWAVYUuTo8oL0JhxWl+W6F66+/viKYZSGolwujJ0iIrlFIUQ0lJODoo49OodFLLbVUikDKFaola0NgkKsxY8YkCwerkNICLBs5E3BP+tKVbREu0UI56R63kHpKXbEG9SdR8YIh4otVjdsPrsssvXSx9NLLpCn9XnbZlCk5V+seHz6i/mRNXnmVVca3apvlBmLPFdZZNa/ktmGxOuOMM1IyvPYEzUiN0HolK0aMGNHOZP5Xy2irjjrqqMpxPc+QX00tL8eXY8g9aD35dirT0WO/n3LKKf2ScyeISuWyxZdAoGMEFHfLbgKpvsOK0jFWjbjkww8/LLbddtv0MEYAmqW6sMgxb7vC3GXoVHRQKQOEpexGYtlgmTFosfLJTfLYY48lC05vkzHZhrnOcsSUfpx++unjRAsRJosqKucU6k+iQk8mwklNqauvvqa46sqrkoiYkNh05VVXJf2RSL5VV121ptvaObo2CkLW2hBmJGHYsGHFnHPOWcw880xJ2E3bZP7EE02URNnV+2NtsxxJIeJWwbujyXLRa+pg5caC4vzdH+p72Zf7Rt99L08Tfm3Cyu+nn3km76LPPoOo9BnUcaBmRICpXppv/7RCVr1FRmsuBJjic4E+OVG68sbfqGeKwHgbFo10/vnnJzGvGkpSjZdrI7HCGGgNVAYm0UjEvDknTD3rFXFLXXbZZW2ihRCRHC105513pv8jeYZy62+iIimjFxC1oeC3wahRxahRG3w5jUrJ/uC5zrrr5C53+omoDBkytFhrrbU7Xc9CbiKk0rOFpZY1BnlC5LhgVltt1WLYsKGpAnZ7O0NUWG9qbchWmahsN2a75Lrz/5CJitpQNFYi3RDkPL3z7jvFEUcekfraH+6hICq1XuVYb8AhwIriLceDRDXaVhjgBtJF9MAVaeP6Mak/9dRTLX/6xLysR3QVN998c3HyyScnNxLxq6KEsDAprGiQQ2w22WSTZOngtpGRV7h9T5ukenRA2erD7cCVko/PraGpfdVfmWmRAlYLblwJALlSEK08+c0NMmyWYcUKK67QLiTcdYhwjvZDVFgpJM3TEEGuHPdhucmcnLMCw2jFFVdMeXtspzq3jMmwUv6Be4eVA5kias4N4RNOL5Qe6VFLy36FxJtcS0QjW7BWWnmldK55e30i6C4TFWH6HTW1tvQpiEpHCMX8QKAPEWBFyQMcN4HMndGaCwEkMwueVV4NklkU8qOwpBCDGzy9kRsARSNJZJdJhMFrgQUWSBaGQw45JEUj0TCw4Pztb3/r0o1g8KR3yEnk8jEQpQcffDBVyu0vouJEECVEAAnoaFpvvXWLH/7wh+2eNyuVc8ph7WOJypCKRQVR4Zqxzmuv/bqyD7oiRGWeeedNGh8uOxofhE71cOsjkJtsvEm6Dpm4PPrYY5V9ZKJihppXBNncOyxASCl3IBJ01dVji/mtvFI1UdmpS0RFHiH9CqJSuQTxJRDoHwS8UXiL8Q9J1BgDXP9ch+4elTUgk0yuOjqNaJ0j4B4nxmUdYFmgwfJWL3lhmcAQ9SIcBlh6H9YaIdje3Mf3fyJTs/+p8sRaKXqoP4oSOk9FJkVUEa7S2PhUtZdg1O8TTjihMjlXViDh47mJGho+fNGkScmata+IyliLinWvv+H6dN4nnnhi3jR9Ej/vs/fexfTTT99GCI38sPSUG8sO7Fyj3BJRmWZQ+ilayXLZklU395ulxzwCWW2lcYjKVxYVxMm6xObE3V7WCHjzJOcPPKwTRCVfgfgMBPoYAYLAPMCxosQA18cXoA6HYynIJQwibLwOgBZFGqhoTAyUIkAQFEQFYcluHdYRLgiiVMsRfETAoGrAe+utt9JgbpDLkwRymQTRrvR145bKIeEIGcIkWZ/+cYn5bVpuueXSp3NeZOFFEga5ry+8+EJaX3h2bmM1KiwqXxEVJGbY0KHFggsuME79JAVLWUEUwJSXiZVLRWvY7LTjjmke4rLyyisXiCItSW5li0omKohGbp5pzqdCVDpw/XAded5Z9+f33ZdcX/qkD3mCFb2T+Y7V1y00Kn2NeByv4RDwUM0RPccdd1wlfXrDdTQ61C4CRIjl5HuyqUbrXQS4gHI0EjHvgQcemDQWRMs5XNnAh8BkV4bfeeKauOSSS9LLQX+4frhkhP4a/GkvRFVl1waiJeP0+ef/NEVasbKcd955yWVWziSMKDifsmu4WqOSrwJLh3WrxfgsTYgeDdWSSy6ZpsGDBydSwCJonhcnuNZCVBSTzA1JdMyOiMq222ybiAcLCoG1dZ986smkb2FRYkXKExH2hqNHpz48+9xz+RB99hlEpc+gjgM1GgKSOMlH4R/UGxMBYLTmQkA6d+GwrqE6PFk42Fxn0Tq9RQD8Xxn4L7jggkRguJHKdZFcKxOXBy2MQbI/mv7pB5Ig5Juw2G8umqwrES6sOCOSoPZRbqxErA2iaMoZjbNFZe2SRcU2NCz2jdyU23bbbZusU3DLzTozTD99/pk+hUvb/ldPVLl+Bo11/XDHWL7xxhun0PBTTz0tJZAz7+KLL0r7qHb90B8hQlxYSJt1Rft01C6++OK0Trh+OkIo5gcCdUbAPz4tCvO1t4dchK7Oh4nd9RICrCg5j4QBhjAzWmMiICIm51gxGJr878kJ4wXhlltu6ZeO06cgIAZg948SBvp27rnnpggx31lWWI9YOfbae6/UT1aVLbfcKq3LmlRuY4nKV1E/eRky4z5lXSpHVRHQegYR0X5rw2+lcgpE4FwsRL5InhILtmV5ac/1g2gQLXMPWQ/5M3Flccfde++9qRvVYlrXRX+1FZZfIblNOyP6KmnDJIhKvqrxGQj0EgJEg/zB/uE8JAdCyGovQdlvu/Xglf/CNZSGPgsZ+61DceBOEfA/5lqxfG299dYpHDlnWuUy8tLQHy2H2wrdpkdZZJFFUz8vumisBQKJEBVFRMoiRKSqcZVsteWWKa9S9cAusmro0GHFWmt/pVHJ5/a9730vuXCk7ddsCxOkbfXVVy9WWXmVYrXVVktuaBoeIcusOJaJ+mnP9UNPU7bo5GO197nKKiu3CU/O62R9C/dKZ+3iSy5J+ARR6QylWBYI9BAB+RGYm73BMO/2dsbOHnY3Nq9CwCDgYWrQM7iEFaUKoAb9yU0ip4uU7dWtPxO+nXveuWnwp+sgJmVhYbVYd711i48+/ihVtxaq7X7jViw31oj26v94psw004wpQ3B5fd+9JGWS4ndOgMeCU24STE433XRthLcV18+vvnJP77nXnikU+eOPP0pRVwhLexNrsagswmA5Y6qb0Gi6ItFbLJXcQCa6lDyJkOJWgk8Oxa7eT2/+Do1Kb6Ib+24IBNRR2WqrsaZavuhmSZ/eEOA1SCdkYKUXMGjIfdHfxfkaBJam70Z/EZVDDz20IqCXR0YU07TTTJPuLxYOGVpFjnHBEARzXSlhIIlgdUNOrC9hnsgo96hBvbMms/Cyyyybcp188MEHbVZVP2maaQYVn37yaRIsy1+TCXo5PNn/gWMR2joHLqNZZ82fvn81WW5dVuTcJAbcaaexCRGdm5a1NNbtaAqLSkYwPgOBOiFA8e7thNlU0qZazaR1OnzspocIsKLkB/L8889f8bf3cLexeYMg0F9EhS4FWTnvvJ+kqBhE+PrrrkuDsyzUo0ePTlYG1g7ZYBUlNHDbrrrRrBCl5oFdBOH4Qq5ZmaadZtqUu6V6f0jJ/PPPl7QsK620chLt2jf9SdkqpW6SfCtEsZLqSWzYdjqiOPzwI1JWbcvpVtQCyk20o/2KSMqNGxURqZ5kuqXlE/7/yiuv5NX77DMsKn0GdRyoLxFgZt1iiy3SPyL/c3/E/vfl+bbisVSnllPCw9SAVq0HaMVzHmjn1F9EpT2cuUjOPPOMpEVRgiBXFs7rErKWhbB5vs933nknDe6eM1l/U17e3nf7b8/97BhyoGiOKTcKXVa1NYcQttoa095x8jz7LffNsZ1nrU1kkn30dZVu/QuiUutVivWaBgFvPawoBGlhRWmay1bpKLcOkSWCIjRUCHK01kSgkYhKayLcGmcVRKU1rmOcRVEUtCjZREsxr/5FtOZCgEA2W1Fk5Mzhk811FtHbWhEIolIrUgN7vSAqA/v6t8zZy3IpVI8VRU0OIYTRmgcBVpRc8p5odnw+/uY5s+hpZwgEUekMnViWEQiikpGIz6ZEgG941KhRyU0gB0FXfK5NecIt2OmHH344hRtz9bCidLVCbwtCMmBOKYjKgLnUPTrRICo9gi827k8EJGyS6VEqa2XrZWiM1jwIEMeKWEBQJLTqjyq6zYNWa/Y0iEprXtd6n1UQlXojGvvrdQQkTZJa2gAnc6NERdGaCwEJtlStdQ133XXXlFyruc4gelsPBIKo1APF1t9HvxEVoYfHHntsoVptTK2DgXTT1WF99fw3kt5a5VWJmE4//fR+CZWr5/kMtH3J03DkkUcmgiJRlYyl0QYuAojKVVddNXABiDOvCQG5lLpyn0xQ015rWMlblNoHt956a8roJ6tfTM2NAdO9Wh5cMvVuEiSpveENXNKi0KLUG+He3x8rihTeruHuu+8+Tl6I3u9BHKHREFApWOKxaIFAZwiwqFx++eWdrdJmWd2ICtEcEV201kJANdGf/OQndT0p+5tqqqmKSSedNFlR6rrz2FmvI8CKIvslgsKK4oUkWiAAATV0VA6mVZI7J6bAoPoecG8stdRSXdKw1Y2o7L333sU999wT/60thoDiVdWlz7t7im+88UZ6iGUryuuvv97dXcV2/YSA2iRLLLFEIimKrZVTgPdTl+KwDYSA+8E94qX1kUceiSkwGOcecG+oz6YQZK0tiEqtSA3Q9epFVFhRWFBMuWDWAIW0KU9bOnIaNCRTQTdpwKMFAoFAINAXCARR6QuUm/gYPSUqrCbrrLNOGuCYhBXDitZcCKhPokorkqISbFfqkTTXmUZvA4FAoBERCKLSiFelgfrUE6Jy3nnnJQvKZJNNFlaUBrqmtXZFZeqTTjopEZQZZ5yxuPrqq2vdNNYLBAKBQKBuCARRqRuUrbmj7hAVVpORI0emAW799ddPZdVbE53WPaunn366UKWaFWXMmDGV6q+te8ZxZoFAINCoCARRadQr0yD96gpRkUn2nHPOSZllWVHOPffcBjmL6EatCLCiqFD9ta99LVWsvvLKK2vdNNYLBAKBQKBXEAiiMh5Y//3vfxefffZZl6ZyAb0//vGPxfPPPz/O9NxzzxXq2lS3O+64oxBB1ShVZmslKi+99FKx1lprpTfw0aNHFxHRU31lG/83K8rKK6+cruFWW22Vqlc3fq+jh4FAINDqCLQkUUEU/vznP9cc/sQSoHBae7VlRKtMMcUU40zTTz99yiEx9dRTj7Ps2GOOqdw3hx9+eMq6yoReniaaaKJi2223TeshMi+88EL6fuihh6b12iMxlZ324ZfxEZUvvvii+PGPf1xMPPHEKcNsvUKZ+/AUB/yh/vOf/xSnnHJKuu8GDx7cpYyRAx68ACAQCAR6HYGWJCo/+9ndxdxzz50qt44PwUsvvbRYeumli2HDhhXLLLNMccEFF7TZ5L777iu22267FO0g4sEkf8QiiyySHuyjRo8udtppp8py6950002Vfey3337FhBNOWBx//PEpE98VV1yRXCLTTTddqhJsRcddaeWV0jbKD0wyySTFe++9V9lHf37pjKiwoqy55poJB7V65EmJ1lwIIMmrrrpquoZbbLFF8fbbbzfXCURvA4FAoOURaEmictWVV6UHL1dEZw15YOWQF8KAK1uq32eeeWZnm6VlJ598clr3rfE82BGVQVNPXfzfv/+dSNARRxyRtl9hhRVS8jM/ZOlbcYUV0/xmICrewBEYVY4nn3zycchdOpH409AIcGmeccYZydqn1hLCHi0QCAQCgUZEoCWJyrXXXptIxAYbbNAh5mrNGGSVo8/ZNV977bVimmmmScTl73//e4fbWpBJziuvvNLpemOJyqDik08+ScRk6NChBdfUcsstV4iI0eSooA3QGp2ocFGtscYaCd+NNtqoVwsWJkDiT90RePHFF4sRI0aka7jJJpsU/heiBQKBQCDQqAgMWKJy3fXXpQe1IlrllpOTlU3gaptw2Tz55JOVVY879ri0/ZtvvlmZ196XTFQQn80226xYcIEFmo6oqHCsnXbaaQVtzaBBg+INvL2L3c48Gp4DDjggFV5E8FZfffVi9REj0udqq62WUkk//vjjifx1pUhXO4dKs9y3yCQBuHbhhRcmUnz//fen3ypU0xPRVl122WVpXv4j9TnL4pZbbtlGzC3dtfnHlLRXeZv2Pj/88MOCWJxOLFogEAgEAj1FYMASFQ/zo446qjBI5EZQO+ussxYEhX/961/z7OL3v/990pnsuedelXnZokKPIm+It1QP5/fff7+yji+JqAwaVGSiskCTERXhxmeddVYavLjFvIGPj5y1AWCA/yDQXnTRRROpFfIrbJvLbBLTJJMUv/jF/7d3HmBSVFkbXkEyAgJKkCRJBAUBJShJlCjBAEhQkKiAgaBIEJAkrguSxMiCrkpSyQjuIiBZBBRxMaMYWBO6uyLqrvvf/3nPcNqaprunZ6Znpqv7nOep6e6qW7dufVVT96sTtzjVAFLQLbPSqVNnlydPHqeaPipf4wM1YcIEd+2118o4unTpEtAieo/HvawO35ArFSLRWJ+WKVXb45gLGVq4cKGusk9DwBAwBDKMQNISlWDEMMfgTMgD+b5TfiTahpTh2PGHDx+mqyTXBG2ZfHgo58qdS/a97bbbAm34AlHh7RWi0q1bN1ejRg1faVSoy8NkR7Vcy4uS6tJG9QOi0uhU+nmIAAQYTQPLN9984/D3efrpZ1Luu/vuC/R5/Phx9+qrr7r169efVnYA/xLS2rPtwP79gX0+/viIkBLuy+eff17MjRxv+vTpgQzBS5YskWMeOnTIoWXxEuvVq1fLONgfsqMaxA0bNsh6HKa9AmF95ZVXHH2pQOr5H6APqqR+9llq4q7t7NMQMAQMgWgRMKLinDxo1e+iZ8+eAbW5gghRwdF2xPDf3zIfeOABeRiPGjXKzZ07RxwTcbBlcvGKEJViRSX8+drO18rbtUxePvJRgaiYZAwBrrXWyVmzZo3cWz/++KMQVwr9IYsWLZJ7SR2tSVWPg7eGfKOBYfKnL8gNmg00M2j+IATXX3e9+/bbb13Hjp3kN+vOPfdcCX+nvhK/mzZrKr4ohL1rlA/rIdGY9BDGx7py5cuJBhHTFAKhYb0SlV9++UXMWYyL9SyQEgjU7bffLuSddYxfQ/ClI/tjCBgChkAGEEh6ooJfAE61PFghGqEkhagUdcM9RGXK1BQflbTyndxxxx2uyFlnOez29erXc63btJZD1K1bV9LM88NPzrSh8LF14RGAXGgqehy1y5QpIySiePHirlmzZrIj2V+5//ABYbKvXftiydGDORHNi4bCf/LJJw5/IdpCkCE6M2bMcOT0WbturTvy8RHxRyEcnkgy2umi/iiYgViHdgyz50UXXSS/MVtu3rxZvkOKMEPRjv8PNCt8x3SEPPjgH+X3xPsnuo+PfOxGjhwpvx9/4glHgkPC9Wk/fPhwC3cOf2vYFkPAEIgSgYQmKvoGGA4LHtY8UCEKmnAtVNvficrwwGaic9jXq/YObPR8ufOuu2RiYtLhDfjWW2+VrWhw8BVAatWq5erVqyff4z3qRwZpf6JGQLVn3CuXNbjMXXNNe6km3bp1a9GS0BGO2mxXjQr32/bt2x0mF4gI2hG2k/NEzTDcS2QwxpQDyVC5tnNnaUt7tBksfOcY77//vnxv26aNNpd1kJ9PP/k00DcO5pgqNakhCf3oA/8kzufS+pe6M3LlctMemCZ+KEpqGjZqJP1q8jgceU0MAUPAEMgsAglNVFBxHz58WGz8OLyykKTs2+++kxTvqK5J9AaJQB1PmDJvhCz4DqigDcH0w5ujipp+6D+SkEoeEqRvwnySRO7QO4fcV19/5YgK4Q32zjvvlG6MqERC03/bmNjV9LNr9+6QJ4DTKUQA5240KsOGDXNUKyZCrGvXLq569epiitm2bZvsv/GVjUIaiL5iv0qVKok2BBMSphw0KmrO0fsUrQ1RPbQPjnTTQa1atUq2U4QQeeyxFCKPgzn74WNFLSDGg1kHTVGDhg1FM4SJacTwFCKvjuZUzzYxBAwBQyCzCCQkUUFdzYM13DJh4kQ3b+68wHYe7hKJkS+fRGJgCkLNjjDRkCUWooK/iQpqevqHhEA2mGC8C/upQHzIlFu5SmW3ccNG2Q/HXS8Z0rYPPvigOzPPmXFTZyVSZlods32GR8BLVJYtWyYmwGPHjjkWHE+Z+DHLcC9RDBDiy3fy7Kio/xREY+vWrY6yDlpPSu/DvHnzyn5/+MMZ4titUT+UcKC/Z55+Wu5jvqO90/sT7UfNmjXdzp07A6YfJSq00czD7Ed4PUIOFsLUNf8K9/Hy5culD7bjeE17HLFNDAFDwBDILAIJSVSwqWMn79u3r6S/J629d+HNkQgHtvfp08dRgA0nWha+oy4nVJl2+AcQqcODl8if+vXrS8p9fAxYV61qNXnw16lTx7HQHv8ATEIkeSO0V1X3hHlCZnRyQf2PJgdHSDKDMpFdccUVki0UchMPYkQlc1eByZ77gnsFMsx9gyakaLFirmChQlJu4YUXXpDtTPBM/rTFl4XQcMw7/GbhvlZ/FvL9ENnTq1cv2Yb/C/fQddddL78xJ7I9mDRwz9MXDrmDBg2S72hvcNJdv26d/EZzooK5KX+BFKfZdu3ayeoVK1ZIO/4v0P5AfOhTs9vOmj1LfpcuXVqOr33ZpyFgCBgCGUEgIYlKRoAItQ8PZNTbOD2i2ubhTgZZFt40WUdkBL9pw9KkSRN542TC2b9/vzywMS+tXLky1SF4wF9Uq5Y4S6K98UZQ8EYbStuSqoNs+mFEJXNAQ1SGDBkiBFRMJQ0aiCkIp1YI7caNG93OnTtk3cKFi+RgmG3It0MWY8gzOU3YF20KQj0qakUpgYG0aLVq/FU6dOjoqlWrJpFo5GhBO6P334kTJ9zYcWPFfENkEQ6yZGRGdmzfLuPAXOQVnMwxX6GdUUGDwpggORAxrz8KmiLIPueAWdPEEDAEDIHMIGBEJTPopbEv2pO//vWvDufIYCHEU5PKMZlhQtq+fZuQGw1bDd4nJ34bUckJ1J1U/sahNZRAPtCgoJ3B0TUUqcWkhEkynHD/oTXMrHz//feiJQzVD+Yp7m0TQ8AQMAQyg4ARlcyglwT7QlTwiTDJeQSOHj3qqK+EJgW/FfVDyfmR2QgMAUPAEMg6BIyoZB22CdEzRIVoEmrIYMoyyRkEiBZDi4LTLPV6TFORM9fBjmoIGALZj4ARlezH3FdHxBmYTKkkrlMnTJx+Y2E28BUQOTRYEgqSvwTs8YUixN7EEDAEDIFkQsCISjJd7QycK0QF500cLnGmrFKlikyaOFGSjwMHz1A+Ehk4lO0ShABhyyR2o+4Oid80JDmomf00BAwBQyChETCiktCXN/MnF+xMi6MvheiIFqH6L2/6RHfMnj1b8nRk/ojWA1EzWiCTyBoSEpoYAoaAIZCsCBhRSdYrH+V5BxMV7274rUBQNPMq/hOUBaBKsJmGvEhF/508KaSuJ6EaVY9NixI9dtbSEDAEEhOBmBIV3rRNEguBSETFe6b79u2TzL1a0ZeEZRSlM22AF6Xw34O1KN76PeH3si2GgCFgCCQ+AjEjKiR22rJlS+IjlmRnSHbU9NRsIa8GhfI6duwoWgFMQyTBow9KEZicjgB5UcjiSo0e6uSQA8XEEDAEDAFDIAWBmBEVCvZddtllYlsnBbctiYFB7dq1HRNpRoSMu9SS0RIEBQoUkPuDJHhm0nBS/JLMs5A5sseaFiUjd5ntYwgYAomOQMyIChWG9+zZIxVaKZ5mi/8x2LVrl8OkQz2izAg5P6gYPXDgQPG/YGK+4IIL3KRJk6SadWb69uu+Xi0KOJgWxa9X0sZtCBgCWY1AzIhKVg/U+k8MBL777jvJy6IVgSEtzZs3l4J24VLGJ8aZp5wFxSYpkMl5U+uHQoMmhoAhYAgYAuERMKISHhvbksUI4Gg7ZswYV7VqVZm4KSQFl2QAACAASURBVLRHteDXXnstITUMFAakECAkZeLEiY56OyaGgCFgCBgCkREwohIZH9uaDQicPHlSTEO33HKLK1y4sEzk5GbBsfTDDz/MhhFk7SG+/fZb16dPHzkv/Lgwi5oYAoaAIWAIRIeAEZXocLJW2YQAppF58+YFcrPkypVLCvGtWLHCEVHkN2Hc5cqVE5IyduxY06L47QLaeA0BQyDHETCikuOXwAYQDgGKIDK5V6xYUSZ6crMMGTLE7dixI9wucbP+q6++cgMGDJBx161b1+GYbGIIGAKGgCGQfgSMqKQfM9sjmxEgIgbNBGn7qXujjqiPPvpoXOZmWbdunTvvvPNknOPGjTMtSjbfL3Y4Q8AQSCwEjKgk1vVM+LP5+OOPJW0/ETMQloIFC7quXbs6HFV/+umnHD1/IpoIwWZcaFEo2GhiCBgChoAhkDkEjKhkDj/bOwcRwJyCeYVoIcgBviDjx49377zzTraP6uWXX3aVKlWScdx9990OB2ETQ8AQMAQMgcwjYEQl8xhaDzmMAFE1ixcvdtdcc42koYe0tGjRwj3xxBPuyy+/zNLRcezbb79dCEqtWrVMi5KlaFvn8Y7A008/7ci2fOutt4p2EQ2jLYaB9x6g3A7V4UkmGq0YUYkWKWvnCwQ++OADd//99wdys2Aa6tu3rxCIWKft37hxo6tQoYKQlHvuucedOHHCFxjZIA2BrEIADeeDDz7ojh496j766CNbDIPT7gFqvmGu/8tf/hL1bWhEJWqorKGfEPj5558dNYX69+/vihYtKmSC3CyTJ0/OtGno+PHjkpgOzc3FF18sOWD8hI2N1RDIKgTQLi5btiyrurd+EwSBYcOGueeffz7qszGiEjVU1tCvCODkunDhwkBuFggGKfxffPFF989//jNdp0XNoipVqgjxGT58uGlR0oWeNU50BCAqzz33XKKfpp1fJhG44447jKhkEkPbPYEReOONN1Kl7S9durTjn2bbtm3u//7v/8Ke+Q8//OBGjBghBIWCiq+88krYtrYh+xB499133ahRo1zbtm1d586d3QMPPOC++OKL0waAmrl79+6uffv2kotny5YtqdrgW8H1vffeex3O0BMmTBDVdCgfp//+97/uySefFPU1/en9ox0SmUY1+aycsAnZf+ihh2S83JvIsWPH3MMPP+wwQ4LBokWLxH8qFB461lh/GlGJNaKJ2Z8RlcS8rnZWMUYA09CGDRtkssmdO7cQkNq1a8uDnge+VzZt2uSqVasW0KLoxOBtY9+zH4G9e/c6kgCiISPyK1++/IHriB0cofJ3jx49ZD3tyHSsn4/MeyQw6CuvvDLQhu26UIfq1Vc3B9px7Tt16hTYfsYZZ8j3M8/M7cjrg7z66quyDvKUVUIoPiH6nPc333wjh2nTpo0c99xzz3WHDh0SYpY/f35HTa3sEiMq2YW0v49jRMXf189GnwMIHDlyxM2aNStgGsIBlwrHq1atcvfdd58rUKCA5EXBedYkfhDo1auXTMz4HSFkA27ZsmWqdThWQzogoWjBcPBEu6JERCfxdu3ayTq0KVs2bxFtCNoS2p1fubL7+uuv5RhoXFh36WWXus2bN0stKko+aH+ff/65VMTmNwkKvYKmheOF0nBAjt966y336aefeneR7xCRt99+W8auG3/77TfxtTpw4IAkFCQkn2Pi3H348GH3v//9T8a2Z8+eVPmFKIT53nvvuU8++US7kk8ql+N7hYAj55ERMaKSEdSSbx8jKsl3ze2MY4gAb6LkYsmXL19g8uGtNdhUEMNDWlcZRODG7t3lGg0YMDAwsaLxgEAwGX/11deicUFjFpx8b/ny5Q6HPggAokRl6dKlgdEQJdawYUM5xvxHHhHNRYGCBRxaincOHQq048sf//hHd8MNN0h/r7/+uuxz3XXXSZsPPvjQoe2A8LJv8eLFxcxE/yyj7hnlMEGyvVChQq5bt26OsHdk9uzZkh8I8sy+rVu3FjIDEaH/li2vcpyL1pPKmzevo/AlUTd33XWXq1GjhmBBXziXX3LJJS537lzS15AhQwP5fnr37i1kHHMSzueMISNiRCUjqCXfPkZUku+a2xnHEAHeVPFzwETAQ514f1TpvK02a9ZMfBPUrBDDw1pXGUAA091ZZ50l14YJGvPN1ClTHBoyZOfOnbKtcuXKDo0Bgs/J1te2ivYCkqLaDSUq+HV4ZcqUKdIHD1ZMgNwHDRs08DY57TuklnYQF0RNTzNmzJCaT40aNZLtaEO2b98u35s3b+74jQaPew8fGLQr9EO0Gr5VkBZKSECk8afCLMV5YwKjb9qWL19BfFP+/e9/B7RLkDYWSFC9evUc/jiQNNpzPATCw28I0eWXXy6FQWVDOv9klKhAvND28BlJ0pNigL647vjz+F04b3yjEkWMqCTKlbTzyHYEeBOvWbOmPLDvvPPOgCpc0/YTiqwPcyYhavokwkMw24GO4QGZgIcOHeoqnCpcyfU5++yzhVQwgfP7oloXBcwff/rTn2Qd61lwxEXUzBNMVGbOnCntmNi53uxzVcuWEc9Aicr1118v7T788EOHCQZzCk7baDzo5+UNGwJkCo0K+UfYDinBBwUiRbvChQsLoUArRDZmqojjY0WCQbQsRK5xH9K2cePGgbG1anW1rMPcNWbMaPk+adIkITacC/tCwtm/c+cUv5uJEycG9s/Il4wSFfC56KKLBGM9LoTqpptuCkTmYZICu8cff1ybRPxEK1W3bj03evToiO0wg6FJPXTonVOffD8kpjVMainbUtbJ93dSvquZjnFhTvzzn//sFixYIBGG1CZT6dOnjzhc6+9Qny+99JJ7ZN68UJtkXceOHd3gwUPCbvfbBiMqfrtiNt4cR4BEbRQP5EFPGvw1a9aEHROOkv369RP1Pe15q506dao8zMLuZBtijgBv30zqb775pvTNxM3vG7rcINexSZMm7u9//7tMxsWKFRNTCA3xESFjMaYNrh8h5ogSlWeeeUZ+6x+tgA1h+fspP5By5cq7Ez/+qE3kc/78+ZIZeffu3UIE6FtNP/g2oTGhUGX9+vUD4e1MTgiTm9auYj8mY7Q3CPciWZbVCRjtytq1ax2aPyZ2zEVMyPix6L6yo3OuVatWsu7dw4ddr143yXfGUL58eVehfAXR1IATWiUmQvaHJGVGMkpUCPvn+N4kYK+99pqsmz17jgxJtVv48niFa09VdcxrV1xxheOcwAzNEH2ysI5rwMJ32tE/ArnTdun5bNq0qeyvxJR9zzzzTOkLjBH1X4IU4rMEzkSf3XJLH/EFog3aMTS37D9mzJiQLz9UkOd6piUQK6LNuMfxsQsODPDuj7YJrSRtIYXbtm87TWvD2OgT7SRkkoV7nP879dvy9hntdyMq0SJl7QwB58R3AUdLHhI87NQ3IC1waMcDAZ8BfbjxcHzqqacCmpi0+rDtGUcArULZsmXF9LFjx45AR0xiXA+0X2gldAJGQ+bc7+HnaDtoh/MsokTFO1FS6BJTSO4zzxSCgymBCY79ILYqvJFrvan9+/c7FtowIeGgSh9oLgilRjgm21evWuXIpAzxQNvCMu2BabKtRYsrxRkWokL/TApMKOwHWUELgmkyGqLy0YcfuhEjR8q+3LNoX8AGnxXOkd+tT0UMMfbMSEaJCtpMzk2TgIELvjd16tRxV199tVuyZKmY+dB4QvDwA1LBzAX+aJ7atW3rWrdpLXmSmNi7dOkiJjj+T8mdxNK6dSv5xOyGQCTJs4Q2jYVJm/uANAQlSpRwTz31pIPA6nY+aa8pCjTKC9Pc+++/Ly8vPXv2cC+8+KKcE2MeOXKEQ7MC8dVIMTQxKpwDySnBYO7cue6NvXtFw4ZJj+Wcc86R8RB+zm+0bytWrtTdhWDgk4QpkD50gZjyTAoWrnutmrUC7bQ9RM6b2h5zEybv4H5pX6pUKTf09qEZet4ZUQm+IvbbEAiBAFoUbP38wxEpwT9uRgV18NixYwNvyrzBDx482DGBpsemntHjJ+N+vOnphI/ZBPx542YS55pi4kB46KqPEY6x1Bxh8tMHcwqB+V37gFMqGg0mKW0zfnyKHwf97d61y3F92cabOZOLOrIShcS4MNGw/fobbpC3ZiYmxsibKJMskx/bMb+sX79evhPyDMnSN/CBAwdJe9rh04KmY+mSJdKWCRiNUvXq1eU32hSIDG0haCpMMKxj8qRvJhvOncyx06ZNczgZU16CMTNB0RZzWWYkvUSF68NEjgaC43OtOnbq6KY/MF1+43iMbw3bcHBX7LzaBULQwZdIPZW2bdtJqgH9zef06dMDfkPe9eG+t27dRu6DcNt1vfou7di+XQggIfOYI9GucK+ULlNangW0h2xyP2CCDCVTpk4RQsv9zDnTVokNv9Gs6e9OnTsHuuB+ZzvXF1LFfTXuvnHi08Q+3ohFtCj4OtGe+x8yvHjJ4oBWh/sZkyoCUcGvibYQbwg6OYL4zrVhPeQvveVDjKgELp19MQRCI8CbFG+j/JNRPM37ZhN6j+jWUjEZFTZvTvpwRT2PaUj/8aPryVpFgwCTNRMuzrJcSxYeshAWzAEqEEk0K/pwRo1OzhNU9+qTwZsu/aCl4e2VyQbnXN6ug4W3ed7yNf8Ob5aELWt+HdTj9AVZRXgLPqfkOeI7Q5+YDtmufTPJMG4e/CyYCNT/4bHHHhMCzHp8b5gUUMVDLtq2bSOTPNolNDf06Q2JJpKHdYQrI5gC0B6ihWHB/KWmASZ52oJVZiS9RIX/C8appAryBwFlXGiY/vGPf0gUF9cWjQWkjDbgoAJRwQRbpEgRV+eSS1zNWimaAjQs/J9zznx6/yfx70GjFEnIg0MElJrhwrUNEJUdO+RagyPaM3xWOA+w5d7TCDIl0dof54gvDuRChf0gl1xrFu5ZzptryW+2EdmFoBHTyu0axab96MsY9z+CuQeswJPxBQsEi20UlkTQIqpZMljbhu9TgwYpUXGQwPSIEZX0oGVtkwoBJi8mJp3QvA5vsQaChw8qXH04cUwyp3LM9Kbtj/XYEq0/CAsTA5MeD+1wwuSP34q2YbLH1wPhgcx37xKuH12vTpjBb5PaL32qoPHAqVbXcRyvto3JlrFpFJLux6een5IX3aZjDfdbz4nxeIVxc396JVxbb5tovqeXqGifD5zSoDz77LOyivHsef11ISlPLXhK/mdxfMavBPIP4VMBO8paoFVBy0AEF9qMqtWqCgHo1bOXfFauUtkRXk6V9Xbt27udHpOh9uX9hPTxf8vS/cbuqUwi3nYQFbQWmK3Q6DHOX//zq9yP+B9BiOlDNRCM75FHHhEtC88kzFwQVYhvOP84tE48P0IJWg/VAOJLpfc3bbk3KRWiZi60LYwFM3UogfyQ2ViTIXIdlKgEh/iz/4YNG6U/NHnel4NQfXvXGVHxomHfDYFTCPCPqv9wvD0HP6izEijU26hytdIyD1TeXHBQMzEEEgmBjBAVCJtG1NWsWUsmVyLtMPXghKqmNjQi+KLw+5ZbbgnAhn8HWi00MwgE7uyzi4kWDadPwr75xNkWQhOt0B4fD7QSqo3BdBhMGCEqmNVILsgn5ARTC+NHC0aE4OrVqyVaib7wl0H7w5i1LyUraHoIr8csyPLyyy+Lwyuavrp167oNGze49etfFr8mNDBo0xCIhZIqTJdo+CA9msRPzxktH+1IwRCNQIL0uRmKqKD5KVG8hPhhaVqAaPo1ohINStYmaRCA5WMe4J8TgsDbRU4JpiF8FHDc1OgAHgLkzEDNbWII+B2BjBAVtCj8f+bJi9/EGeI3sX79OtEwYG7A3MF2zGA4AaNB8WqJmKyLFi3mBg0aKPAxuWLqgQxgxoPc8Ikp6MorI4eWe/G/quVVrlq16rIKLRR+GYyD3D1aLoGNEBX8RiAnEBnMMLTHD4XnDePHXJj3VA4ctH+sx/QEqVLBbENUGv5yHCeahUg3lccffyLgT6L74tMDaVGtn0YXLfzzQt1N8Bw0aJA8lzARYRKESLAPOEciKhChKlVTirSmx2xoRCUAv31JdgR4ODRo0ED+4XEaDKVWzymMMAOQtl/HR2QIDxEeUhAaE0Mg3hDADIB5DVNUOEkvUeEtnIkdMwumEZLj4bzJRMZkS8bfKZMny3eclSErhIKjtdCCkfiYQQJGjrxbfDAg/fiK8b+EqVUXfhOhgxnOSxDCnQt+MPibeM8XzQjmHW/Ul/qoELmEDw1mIPyPKHLJyxH/2/piwjZ+4/uCpidYwBeygj8RGpXFixcLwULTQnQYkUZohyFF/FaNivaD2QfHa5x2IVZ586REAanTuEYW4e+jgt8KzsnUM9OaZmitqDqPRCIqYFmmbBkhmWiFohUjKtEiZe0SFgH+WTXnAlEEhDnGsxANcttttwVU3JiIyBiqTpDxPHYbW/IgwNt1165dJRcMDsKYZ4IlvUQFbWeNGim5YfDRQLvIZI02AgfSKpUrB3w7mDwhNWhGIDFqisC5lN/4hEEOIAVEETHplit3njhI4wPCb0w5+HtE40APUeH4XqLC+aLR8RIdJSrqnItfGo64mHY5P8gFZhjGOHjIECnHQe4VCIJX0MIULnxWKrMMhIT9IB4IxADyxqcKzwnwwkwULNtee03MVpikGPdjjz4m/UFivIIWigVcCp9VWAiWmo0iERXM2uB9/vnnn3Y+3v6DvxtRCUbEficVAkRcaK4LPOn9ZFLhbQyHvA4dOgTewHB64yHlp/NIqhsuyU6WLK9MnCxoQEhqR/QImhF8TZiAyNUSrRDCT7gs2k/6RIOAYOJhIV+RJmRT0w/9Y2ohSy/C/uyL5mT//n0OssRvngPLly9z1G+iXy1dgI+a1+FUOgnxR4hKlSqnEZXgpmr6QcsDabrgguriQMt4K1asFEiixpjQBoUTogNp4/VdI6qGsgj44SBoU2jDs00FzQvrIAteB222owXDF4btaKDwzYPoQVy8x9G+ME3TFqIXTFSCQ9chNmoOG5LOrLlGVBRx+0wqBPinIQkS/2TYpF944QVfnz8Pfhze1Jsfp0KcBXkT877N+fokMzh4rnWyY5BB6KLeDe0Jb+BMbPhOQAbQdhCWzf9Y8MLERjsiRtJDVHRAqpXQqB/WE+5NpIs6oWu/S07lk1GfCAop4ttCyK4K69CekEAPkwSmDxxbyUTM5B2NiOmnyukaleB9GTvh3mibtJgpUT06iePAC4kAM3xGCA9Gw6MhwPQH1jjMXlL3ksC9vXTZMtmH8/UKZmy0T2o6IoqQjMf0T59vHzwo2g3My2hqWc91UxKD9oV1mKUwZWE+4jpzHM1TA8FSonLZZZdKe8xFRF1hViPpnebe4dprqLR3nJG+G1GJhI5tS0gEUD+qFgU7tnrSJ8LJ8nBBdexN24+9mrev4JwJiXC+aZ0Db62o1XH0440bEwTRGeSYIH+NNxeF9sWbN7Z5Hqxoq9jHGyLOmyJZiZnMiMbijZy8EJBCr+ATwIOf7Xzq9/mPzj/NV8C7Xzx+Z2JES4ezKiYDJh7uKc4JjFD3M9FrrhgmNhYIs37XT8Jv8f2A3IChEor0nLcSFSbLH0/8KH2UKVvW1a9XL5BHBW0Efl34jZQuVTqQt6ZN27Yy8XuPBxkZNHBQqrFyD6RHQvmohNqfsYMTNYjwTdm6ZauQIUKFwRMHW55L4NWgYQNZR24VyJQKWim2q+8IZiBy+WCm4vnGvce5c38Snk1b7lkVzGWamI1tGinFdzRJXjMd2GBa1sRxOAejYaEtodMkHyTvi2bppgI320ItlCkI1rTomCJ9GlGJhI5tSygEeKtmQuEhwT+mpt9OqJP0nAymId44lZTx4GDyRa3tnXg9uyTUV9T1+C0wgaBVuf76lLo+PGh5q9MHqbdoHdEZvO2yTR0a+U4fihnkR/cN/uSBqm/gw09VHKYNTpH6oOd302ZNA/3FA+icG5Mdb91McnPmzHGkWMdplQmNSZC3fz0H/ofAkegY3v4xOTIRkimXTLaYaHjrxryiGGFCwF/C6/ydXh8VxQr/DvrFiXTTppS6P0ya3PO8eLCNtPVoE2rVuiiQSZoJmG2cG98hB5RHQGPB+ZG9FwfdGhfWkPPDEZbQZvWF0eOH+mzerJncV8E+KsFtdeyYYMIJxJpxKhHxtoM0Vq1SVZxs1XdGSzxwffT+ZX+uD8nxyEDL+aE1UcE8xLOAa0CumMG3DZZrR/+hhHtj/ITxrkPHDqINwsTM/9inRz91zz73bMDkhX8N/yNgRhsWSgxgOlItTaj+I60zohIJHduWMAjwT0bSJ/55UbmmV/XodyB4E8ZfQFXKPMB4m8WGzSSeiKKF6zBD8JbJteftUhNN6VspDpdggJMhD/lcuXPJhIs5DXOBqqzvufsegUmTYPHmiWMzx5k8ebJMHBxDM8iqfwYTHVk5mSSI9KhYqaKMxWtuRLugERyhJrrPPvvMHXr7bfefX38V7RD5KBAmCjQd7M9+Wh+IbThfMn7Oi2Pjj0UkCxMH4yXLMuGlnAcY6Fsy56BLieLFpQ1v9JwPmEEAmITANdjB88RPJ2Rc/GHM3HdMni1btgz5Jp1RokKYLSZbiArnDTkCA0SvYyhNDZEpaEow72D65XpfWCOFjNCH+nZwXviq4NsBeaEdNXMiCaSO3CWhrp93P5x6eVECv3CCGQWzijesWdtCKrkeEyZM0FWSEA4tKrlZ8HVBU0hRTCUdaLCqVa0qmqzATj76YkTFRxfLhpoxBLCx8kaLzTnYfpuxHv27F2+zPCB5a1SNAcmzeGgzsSSS8ODmmjNx7NyxUyZfSAKqdyVn+ClANtCC4I/ABD1w4IBUMKDSJo25Om7ydkg7VN5eQVvAeqI4EK2wjdrcK7yR0k4JDbV18AngHmU9Zilv6Oa0qdOkpg0aDN5827drLxEpjAuNBZFqaAggA2gt+C75OU75OUAUcGTVkgAcI3hp2KihEBhyaJCsTDUnJUuWEGKi44cY1LmkjrRhPJB+DfvFzwICjK8UeUlKnlNSiBOELJwzakaJCvcx5EuJhY6PT6431zU4FNfbhu+QEbQqei8Eb9ffjJ1jnfzpJ10V8pP/H/pTwhSy0SnHXwilkohQ7dD+cg9omYVQbdKzjnNUjWB69ouXtkZU4uVK2DhijgAPKx7ePJTJ9shDxOR3BFD1o1rW3CxMZFSPZTLy80ONMzz+/fcObQCqf4RJgTToOkGjQeBtGQ2DCgSB7bxZI2hecAbkDRiNDNlKEU04RiI+r+ADBNmAFDAxTDhVfgGtxZo1ayXkdNbDD8t26snwdo8qnLdw9oG4PPvsX4RscE1Q/zPJMya0HWgEIQd6DqjzIQb6m898efMFfqM9grBAJtjW8qqr5E27XPlyQlr+9rdNoo7HtHPttSnp1iFftEWbgDakSZMrxEEbYkC+Dcgt5jS0MpAa2oIbygwICr9ZODb+EWmp+jNKVLy42/fER8CISuJf46Q7Q95GcDzDJku2SVT1JpERYCKmuJuGJuIYyZs56/0oy19YLhMmYaxewXRBSnKSaOmkihqdt2Yc/VhHZAOCedBLDPDTgIAwidMumKjw1o3vBhgimFf0GEzwqjFhHeYT3vwXLlokbdCo4PyJFoboCtpwDbzOqJgfICa6DuKi5AoNBr4PmI+uv+462R/TAI6vaqrCqRpRYnpjt26SMwh/ErQinBuJvDg2Dsji6Ll1a8DZHFMR217xFOfT+jaYW9CosJ1z8fqhyEHD/DGiEgYYW50KASMqqeCwH35HgLwEOD7ywOQhitbAJHoE0KRg4iCSQ00FOOPik+Cn3CyQkdq16wROHO0AzotqgoCEMLEqEcH8Q74M7pupU6fJfky2L770kvir5M+XzxUrdrb77bf/OSVBWmFWD6JJuiACaHDwAaI/SAR+ERALTT6Gj4KG0dIGrMmWCrEmxJx9IAtErLAdbQXRSGRLhpxgmsG0oXksIJkq7du3k33QGrE/S91LLnGtrr5atDT43jAeDS0FgzFjxghRwQF22LBhEvqq4yJdOucDMWOdVzOpWhUizSBabJ/58EwdSpqfRlTShMgaOCf5dtIT/PAHQ80QiFcECMdDi0K6abX/x+tY/TAuNARERWgBOLQQ5HrgLZ2373gVbPtM5N5wTiZEJlHMEV7RfBK79+wOhLUywUN4VZYuWZJKU7Jk6RLpC8KAkyKmoAcfnB4IA8VMg3lEnVPRpoAd5h1MPvjNMBZqpOi4unTpKtlDMZVgsnzqqafE9IRvDG2JVkMgGRAM+sahFlLJdoiGCiYt1hG9g9Dn2jVrZDn+3XG3eMliMflA2ojE0DTomLggc69u2iT74ICuSc8wgSkp8YZ0Q2g5FpocIn74TsbXaMWISrRIJXc706gk9/VPiLOn3D1vnzwkcTZkgjWJHQI4B+KASt4RJltwJhKCdN/eCT12R8xcT5g8/nDGGakcUpl0dezUSMLxFd8NTDUtWlwZSJql/hyYWGjH/cT56sLErZoIXef9JDkXmha0M2hF2MZkDk6EkuL3gr8U61evXiUnClmCYM+YOVO0PGhaIA+0hXSVKV1G2uOTQv9oXQoVLCjmGjXrkApeBcKBhob2EHa0LRxv5IgRohlRvxYccSEgJA5D08P/jaY/x0SGBoc+2Pfwu4flHDA/4byLNorJg21or7hHNFNqWtExOk4+jah40bDv4RAwohIOGVvvCwTI98DbKpEKvIWaZC0CTLaEfZKzgkmKBZMGeRO0KFnWjiDt3ps1b+Zu7H7jaQ3RmpBsTMfNJ9qOTp06SoI8HK+9+VW0HZM45hN8PPDhgLzwneRaJLfi84rLr5CkWJrXgoMTJko7NH1emTN3rqxnO4IphTwYECnuY8KhMVWpkBeE/BUQRTQeaoI59uWX8jvUMXASxkkWsw5mJhKJaebQ7Tu2S94TwntZICeqfYFQ4VALmWHbRbVqybXVsdCOzKP0y3YI2ddf2+x6xAAAIABJREFUfyWbMQ8yllBhwbp/8KcRlWBE7HcoBHKMqGDzhMETpoXa0Bb/Y8C1RDWtfgChbrhYreN+MS1KrNDMWD9osgi91dwsTIiDBw+WZF9pRXtk7Ijh9+KNnmcKvifYsglDZ8H8wyTNvYKzrNcvBOdWzIQQBNYzYZOCHR8NstEScYN2gucUETixFk0Mp/3ic6L5UXQdDrIQIzVlEpIMicDRNjiHie7j/cRJNlyYLtvAK1Q4LYSL/+VwJj58WUKFBnuPHc13iEqypwyIBqdkb4Njf474qEycOFGcHblReZDY4n8MYL28cXpDPrPiHwy1M2+eqMAJkzTJWQRITU/VVkKBuSZoIvBrwRTgTUCWmVEyYTJJEwLM5E0uExxMyYYKuSB3CdoQImK4NzDpMA5MKqzHvIJJhnsUvxt8OzBnHTx4UEwosZh0M3N+4fZF24KWgnPB7FOiZEkx03hr3ITb1w/rcdQlqRlkyhbDINw9QNJE/uejlZg503KD4pRnklgI8CYaKptiLM4Se72qvbGLmy9KLFCNbR+Yf0hJr06YOLVSMZeQ33AaAMLJeUBpCnecNSGgaGt69+4tmhBCdjUsV00yOJSiDcEsg2MrPiUjR44UZ06eLWjdKG8fbahsbJGIXW9oKNE6oOkhMR8J4hJFSMaIpgutkS2GQbh7gEg6tJvRSsyICt73hLSZJBYCePxnhZaDyQ+7OM58hEGGUlcnFpL+PxuIJdEo+DJALvDnwLET3wy0IWhT0XIQCYNDp0bJ0BazDJoRzErUKqHKK5V2SVCHSYbU/+TuoLYLRMfEnwhg/sJkB4m1xTAIdw+gtU3PMz+mRAV1sUliIYBzayyJCqpvDYEkkRRvySbxgwBv+zhp8pZPqCuRJJhWMMmgSUHToeG4qgnxfhJBw1sUUSto45YvXy6RKPRH+vBwPhLxg4CNxBAwBOINASMq8XZF4mw8sSQqRPHg7IgWhX7Tw6jjDBZfDgdNBRoLnGYJYyW1PGQCUkGtICJo0Hig6SJ6RgkIfiFoSEij3qZNG/E/Iw/IokWLAuXniRQihJZ9iRyhWJ+JIWAIGAKxQMCISixQTOA+YkFU8FXAJMDER8horBwyExj2DJ0aKneiOyAiFCrEvEYKdxKGkY0WJ1SIopeE4KRKOHi5cuVc48aNJQEc5hwcnPEtoVovxdnC+aPoQCGde/fuFfOP5iUhkoUKvfGYm0XHbZ+GgCEQ/wgYUYn/a5SjI8wsUaEGCqYCHCd5ew8O4czRk/PRwYliId06ic4gIWgzSNBGdB2mFqJgICJoP1QTguMruJOZFRJCYjKiZEiORmggUTJE3eCgGssQdKJ5yL3BuBgD42nRooXkxeEcTAwBQ8AQSA8CRlTSg1YSts0oUSGfgxY4I+EVb/km4RFAY/HRRx85atQQDk70BJF0mFqoxot2wktCmPxxUCUHB7VkML1QWZf9MOls2bIl4JyK41pOCZFcRLZUqVJFCAvJ1jAzMT7zV8mpq2LHNQT8hYARFX9dr7CjReV/4M03Yx66mRGiQnw8Yab4LPD2nsxRHGiQCPElYmbPnj1u1apVEu49fvx4yVNCwUXSqyteqg0hYgYSQmp7tBGYb6jNwvV48cUXhdBAAggD9oNQWRhNEPkT1BmXfCJECxHtY2IIGAKGQDgEjKiEQyaT61HRUxaeImR8yvfJkyRpFm+YU6ZMCWybNHmSfEcVr8JDnTdPUnETbaELReT++NBDpzmikrETJ1VU+bGU9BAVqvEyXiZbQlBJvpXogsmECsVEtXDNyDkzduxYIRYQDLQdpUqVShWqCz6YRMglgkmG60yoLkXgqNj7+uuvS04ZCE52Z4TNjuuFqYnMrNwjSsxILIi5KCsyxmbHOdkxDAFDIOsQiGuicvDg22KH91b3jAQFRb9efnmDOP9FahduG5MNdn9Kracl+AygYkfVTiExUlB7hZBefQhH++kt7c442I8oDIqhkQ6cvBWsI/kWAvkhdwVCFAbb3nzzTfkdqz/REhV8HihuhnMmY0kEtT4mEzRVOJRiquB6E6pL+mcIGSQD/4/gxGVokjDVUJuFhHaYZCCsFIbjXqaaLv1yvya77Nu3z40aNUqSvHH/Qt7Al7wqyayJS/b7ws7fEPAiENdEhURgPLyYFKIRIhxoT8RCRoRskezfvHmLiLujxm/cqLG0pT0L6nvyTqjgc4BqHr8DXYh+wRRCeyZ2fus2+vQWgZv+wAPyFo6WAtU44aS8bTI5Xt74cjkMb6EcF0Gjwls6k2AsJS2ignMkWh7OiciSWBOlWJ5LcF+QTfCnqizmFEgniQvRXikJIdxW07dzjl6/EMw2hOJCKtEQoFEBf64ZWhZzHA5GPPxv7m3yMN18880BXxzCodE+UsTPxBAwBJIXgbgmKlTvZHLo2aNH2CvE2y6Tf3tP+faMEhUyZHI8Cp6FE97yNO07mgNCMiEJ7EcJdUJEI8nzzz0nbXGajCTY7gvkzy/RGGhNSpY8R97ASZLGJIpQEwVHSyQniArEjlBUJnLqwMSLmQKCgO8GExwmOLQYaDMwt6HdIF3/hRdeKBogtB9cOxbCdinEpyncu3btKm/3EJilS5dKcT60bpAQk6xDAEdsMtaiReS6cI26desm/j2Gfdbhbj0bAvGKgD+ISs+eYfFTksADTUMh0cSEE9T55PHwai+0bTREBR8QjkU+EK+0at1K1r/xxhve1UIuvKGfCxcuknZoSCIJRAWTAm+aEBXMKpAgjpvTRAWNgWpRyFQafM6RzisW2zR7Kvk5qAFDIjmKYvbv319ICISRvCDeFO5cMwiV5gvp0qWL1FqZNWuWaFO2bdsmkUlmkonFFYpNH5qbBSdiTGlcQz4JscaPx8QQMASSAwHfExVyNjBhYTohTwQPs0hEZfOWzUIA0MIEixKVdhE0KhAMHpbUwPHKNe3bn0ZUIBmE5pL5U2XRooXSLhqNihIVHsylS5XOMaJC4jAV/HfwIwBn/C5i7UcACeGaorkAI5xL0ZDhx6DZUytUqJAqaRljQRvCuGrWrCmhupgQqEuDc+vatWvFyRhySvSJif8Q4AUDrVa7du3E/MY1p/gdeXosN4v/rqeN2BBIDwK+Jyrek7311lvTJiqbN0sb/AqCJRqiggNk8Fs36cJRT2NO8JaXx08FTQhv7yr4MvCQJe9Fr169pEIsPhGYF7zi1ahAVIgcySmNCpMBkRp9+vSRsePMm9E3WvULIVQX0oOKn4gXsAATwnEpeufVhqAJwcSEPw5tCHElvJdieIT7MhZ8TSA4sSZO3mti3+MDAV5KZsyYIU7m/C/hRwSJpaKzV3sZH6O1URgChkBmEfAJUekV1XkO6N8/JFHBZ4EHGKpkKjzzcMNfAWG9Tm5KVCL5qAQPZNXKla5IkSISGvzXv/411WYmZd7+qRSrQoQQxy9TpoyrWrWq+EOgoYFkeUWISoEU08+dd96ZY0QFbQYalU6dOsmbLM6N4QQiRfQTyd0ItcZhGNPc4MGDxSSDtgPC5U3hjnOqpnDHJ4FspiQ6w/+HKBuiP8wvJBzith6TXb9+/QK5WYjCQtNnZRrs3jAEEgcBnxCV3yf6SNCHIypoBCg937BhQ3ljhyhADho1bCjrMREgRH6wLRqiAgkZPmy4tCd1uTcHio4xFFFR0w+ECe0MKm1MRMEmCXGmLVBAaqz07NVTtAkQKnJzZKePCiYu0rUz3sWLF0ukEhokkrpBJqiqSwZasA0Xqgs+l112mdT7gbRAdp599lkHsSNKCNU9WFmRQr1z7DO9CKDxIw8LmXz5H2YhKmvBggWiAU1vf9beEDAE4gcBfxCVXtERFZwpeUAF+6hgIsCJEtNB2bJlpE3x4iWEtDCJorFAAkSlXbuIV+jYsWOuaZOm0k+Pnj0dv0NJKKICaWKMO3bsCLVLYN3E+++XdlS7xdTS5Iomso3cHIRtImg5yLGCZFXUD0nIICrDhw93pD/XSYBPNCOYZIiSITQZB1t8SSA369atE80KfiGWL0Qukf3JJgTee+89KcbIfcl9WqhQIdFYWm6WbLoAdhhDIMYI+IKo4BsRjYQjKmgimCx5YydXAw+vyVMmS5es1+Rkavrp0KFD2MNhRoIg0Achr5EkElHZunVrpF3Ff4PzIfwap1oIAEKEi4Zf8/ZYs+aFsj6riAp5VPCrQQNCIi58SsAJooV6nTdZxS/iCdlGQyCbEUBbSW6bvn37Bkg2mlX808w0lM0Xww5nCGQCgbgmKvhGQAjOK3ee+HngMKcLDqr4e3gFJ0vaowUIJ5gtaBPKmRafCLZRYwW/Ej0WnxwPcwdRP7TB4XPChAlSxh5nUJZR997rPv7441SHxhziJVqaVC7akF6y1RJ2TRgu+VsgCSqQGE3whnNhTiR807HYpyEQzwjgO8XzBI0k/7/4RmHixQGXFwoTQ8AQiF8E4pqooLGgYiwPFR4uwQv+EV6h5D3tveG03u18h6jQJpRTKFEobPNmIvUek6ylROcQkeKNSvG2Icsp5hq0EIyjaNGirkGDBg6CsnTZMtevX4p5avTo0bKOdt5FQy1//Pe/JTcIfRMR88Xnn7s6tWsLBiTC8/pzoOUh1wRtY50ZNq3MtMH42m9DIN4RIEoMDSUvEfzP4K82bNiwVJml4/0cbHyGQDIhENdEhSyUhCKGWogEgRB4BTNEWhEiqIPp7/jx495d5TtRK6GOxTr6JfyVY4Zrw3rMIITMeslL8HdNTBe8nt+YhEi9T40ffpPgTesIcX61ataS9WRdxTeGzLT43tAWLQ/J2GIpRlRiiab1FU8InDx5UvL0kOFZ/yfRuGBa1f+5eBqvjcUQSFYE4pqo+PWiQGjQ3EA6CJ/EXMR3Fr6zTn/rJ0XvWFBD44CKHwyRP8HRQCS3wzkYQsSDFidXEt0RookaO9ZiRCXWiFp/8YgALweYT4lOg/TjgIvJl5cOMw3F4xWzMSUTAkZUkulqZ+BcjahkADTbxbcIYEbFfwzHcSLaIC3kPMIHDZ8wE0PAEMh+BIyoZD/mvjqiERVfXS4bbAwRwDxMtBtO7JqkEFMskXfqSxbDw1lXhoAhEAYBIyphgLHVKQgYUbE7wRBw4peGI/0FF1wgWhZyCpE3iESPmtnacDIEDIGsQcCIStbgmjC9GlFJmEtpJxIDBCAlZFTu3bu3RPRhGqLGF0VOKR1hYggYArFHwIhK7DFNqB6NqCTU5bSTiSECFCcllxPmIAgLaRRI20/26R9++CGGR7KuDIHkRsCISnJf/zTP3ohKmhBZgxxGgFpZJFXs0aN7yLQD2TE88heRy0hzs1Cao3279lIjK72ZmymqSO6l3bt3hxw6pT6oIu5N/hiyoa00BBIEASMqCXIhs+o0jKhkFbLWb6wQ+P777x21uwoUKJDjTq6QJtIEFCtWTLQsaFpI208Bz+Cs1eHOf+rUqa5Z06aOxHShhO30O3lyShmQUG1snSGQSAjEjKiQJZaiXyaJhQBZcCNl+k2ss7Wz8SMCmFkqVqzozj333ECiNgoTkpfIm7iNdStXrhQfE81PRIZnchMdeucdyfZMniPCkCE/aDTomxwr5FMJVfaCtitWrJC8SYQ2q1x88cWSi4XK7JdffrkQC5LKkfMIIkNySYREkgcPHhSHXHIq8Z2IIkjKv/71L+3O7d+/X8Z+5MgRN2vWLOkPvxgVkk3S74EDB2TM7P/ll1/qZukLx1/GyvmYGAJ+QiBmRIUCeuQa4J+Ff2pbEgMDah4ZUfHTv3TyjVWJSqlSpdyvv/7q1q9fLyShYcOG7ujRowIIWgiSuKGJYCHkWLNTX3rppe78SpXczTffLNuopr58+XKXJ08e16LFlY5+2YfyGmhGEAgOafi9pTS6devmyHyNw+0FF9RwRYoUcWTXRvbs2eOo20VOFvrCRETa/po1awrJGjBggKynDMiwu+4S7RDEAiERHWNhP9L9169fX75TiBShPEfx4sVl3VlnneU4b8bKOSOYpaiyzv4sZ599tpyfbLQ/hoAPEIgZUaFOzk033eT4h7MlMTCAfJLxNtb1g3zwf2FD9BECSlQqVKjgFixY4IoULerqX1rfffTRR3IWa9askQmaoqWUnqC4KBP2wIEDhXDUrZsyiZcoWUISve3cuVMqo9MGYrF27VpxkEUjwm80J+RXYTvkgz5Vy6HkoEaNCx2kgW1eIcstxU9bt24dyM1CPyxdu3Z1+/btc1oFHg0L2g9qj5UtW1ZCoV955RWH/wvtMctiakKTBDFZuWKFZL3WEOopU6cIccP0RKFVzgsNDr8hUZ999pl3aPbdEIhbBGJGVOL2DG1ghoAhkNAIQFSqV68e0DowiWPCUendu49M7GgrZs6cKVXPaVOwYEGpl9W4USPZjllEhTIVtBkxYoSukmNAPjCpUHlZt6PxGDtmjPymRhcmG0w/oYhKoDPnxOSkdbroC6J1++23i0aE35iVnn/+eelXCRD7U1qD7Wg6MUfx/ZZb+ga6JiEd6x5++GEhPnxHC8O5szRv3ly2z549O7CPfTEE4hkBIyrxfHVsbIaAIZAmAhAV1SKUK1dOJuGOHTsGKox36NBR1jFZU8unUaNGrkOHDq5Xz56i8WA9phX8P1QgAUzw90+6X1b9/PPPAU0Evi6NGzeW7fTHgh8Kx4QwULg0GqJCx5hp8ubNIxqaW2+9NZCbhWOjrcEMxXfIh8oTTzwh6554/HG3c9dO+T548O+V5JXcoOXZsGGDbEcL06hhIxlrq1atxPSFZsnEEPADAkZU/HCVbIyGgCEQFgGICpoMzBnvHn7XXXfddTI5o1FACBtmssdvToVJGvMNPi2YQvLnz58qKkeJysSJE2UXCoBCPgoXKiREpG/fvtInvicqEAiO8Z///EeSwKWlUSFsGZJEen51+uXzqquvkr4Zsy6QIK0WTyg266nyTEV1/GRKly4t2hXMXc2aNZPtaHow79C2Xbv2Okz39ttvi1ZG/XcCG+yLIRCnCBhRidMLY8MyBAyB6BAgQufss4uJAyqEggyxTP5n/OEMiYLBTwTfEvw4mORVG3L//SnaEs19QuSMyty5c2WCh+QgOMlqu2PHjrnDhw8LMcIxFd8XdVZVzQdOrxAEr5ZG+9ZPiAomK9qhpVHBKZd1j85/VBxp8U/hNw6z5FdRx1qICgKZYjsLhRSrVK0q3/X8hg4dKr+vvvpqSfvP/hA7qrybGAJ+QMCIih+uko3REDAEwiKAQ+mgQYOEMKBdQTB74EeCrwlC2C61eUh3T5QP2xGid6iUDDlQrQbrN76y0XXo2MEtXbpU2qF5IUqHPv7x1T9k3fZt29x1117natSoIeYkQvkRon7wNSFiTiOLZEOIP5h3utxwg/jK6GbG1rlTJyFDrHv33Xddjx49XJ06dVyxor/nZ7n22mtlGw66JH+jFhERT2r60TwrmK3wa4HkMFaCHnCqNTEE/IKAERW/XCkbpyFgCGQaAcwykJNYCiQmnGCuIXyYBdKjC6YnrxYl3P7e9YwdMsW+pOonComF8OkWLVpIhBAapRtvvFE0KH/729+8u8v3SGM9rbGtMATiBAEjKnFyIWwYhoAhkHgIkLwNE828efPc/PnzAwv+M5kN+9+7d6843HojhzSfyrhx4xIPTDujpEXAiErSXno7cUPAEEgEBMhyS3RPj+49JOQaXxXMW4QfQ5SyU9D6fPfdd2LKQvtji2EQfA/g44WJ1pvJOa171IhKWgjZdkPAEDAEfIIA9YQgKIRMQ1iIZsK/haR3+LJktVBQkfBnkumRLNIWwyD4HuDeIEUAyQujFSMq0SJl7QwBQ8AQ8AkC+OEQOo2jsJqDSCg3cuRIqSeUVadBLhgin9Cs4A9ji2EQfA9wb5C9Pj15fIyoZNV/rPVrCBgChkAcIED49tNPP+3atGnjcuXKJZqWli1bukWLFqWKNorFUIl2Wrx4cSy6sj4SGAEINNFp0YoRlWiRsnaGgCFgCPgcAcKSqbqsmXxJSke4Mmr4WERDQVSee+45n6Nkw89qBO644w4jKlkNsvVvCBgChoCfESDZHOQEklKsWEpuFio5P/jgg5KbJaPnZkQlo8gl135GVJLretvZGgKGgCGQKQSIwtDcLJrhljwtrMNsFCy//PKLmHdCRW0YUQlGy36HQsCISihUbJ0hYAgYAoZAmghQB+iee+5xFStWFF8WHHGHDx/udu3a5dDCIPv375cSBd6KztqxERVFwj4jIWBEJRI6ts0QMAQMAUMgTQQoS0A6fmojFSxYUEgL9YzmzJnj+vfvL7/JiqslCrRDIyqKhH1GQsCISiR0bJshYAgYAoZAuhD46quvHEUa69atKwRFzUN85s6d2y1fvjzQn9+JCsnJKDgZyqyFiYxq1JHk888/lzpKofansCV1myCByS5GVJL9DrDzNwQMAUMgixCYOXPmaWSlUKFCbu3atXJEJiA/R/2MGDFCKlD/61//Og1BqlGXL18+YtXp3r17O0oakC04WLZu3SoVvjdt2hS8Kezv119/XepDrVy50q1etcq99NJLqZZly5ZJtfCwHcTpBiMqcXphbFiGgCFgCPgdASpCezUq+r1o0aIyYY4aNcrXRIX8Hvny5QtcJqKgqHA9fvx416VLFzn3Tp06ubFjxoovDxWr0ZSo9OrVy5133nn6UwgLGYFpA8kALzRQP508KZmCVbvyySefiC8QRGns2LHynYrXVMhWjMN9jrvvvsDx/PLFiIpfrpSN0xAwBAwBHyFAJej27du7zp07OzLQTpgwQUxCJHhDo4LZZNiwYb4kKp9++qk4CXfv3l20HjgP41jcpEkTcSwuW7asEIZzzjlHNCbnn3++K1eunGvWrJmQkSNHjkiF7MsbXy4amWeeecatW7fOEfKNQ/K5557rihQpIn3wyW/Wk8+Gujcci8zB+fLnlzaFChV0DRs2dHvfeMNt3rxZcM9foIDgTFVsQstZ8CP68MMPfXQXpQzViIrvLpkN2BAwBAyB+EeAqJ/ffvst4kCHDh3qS6LS65SmSDP3or24pn17Ryg2ifCIhMqbN6/DFAMGmHZOnjwpC4BQGTtY4wGZGz16tDgfDxg40LVr107akCFYayENHzHc/fD9D3IMtCvazwsvvJDKl4VMwo0bN46IvZ82GlHx09WysRoChoAhkEAI+NWZFq3Fo48+KhqOAgUKuLlz54jGgksDOSHyafac2XKlqFR99dVXuy+//DJw5fi+ZcsWR/4ZtCVomHCs9cqBAweEqOzdu9e7OtX3Z599Vtp4/VgwHWFagywlihhRSZQraedhCBgChoDPEPArUQFmiudh4smTN4975513BPl9+/a5UueWcrVq1XJvvvWW2717t6tWtarD9ENxvWC5+eabZZuuP3r0qPjuvPfee+7JJ58UEkJIN9E/RBcFJ9Sj/hKaGciQyvbt22Ud+x8/flyceb/99lv5xGwUKsJI943XTyMq8XplbFyGgCFgCCQ4An4mKhTJU/NNjRo1HJE/06ZNk3WVK1d2VapUCWynMjU+KK+++mqqK9q3b1/xXSGr7+rVaxyOt/SpuWi0fxx28+TJ4+bNm5dq/1BEZcqUKdJH4cKFXcmSJcW3Bf+WMmXKuBYtWpxGdlJ1GKc/jKjE6YWxYRkChoAhkOgI+JWooB3BMRazD1oVNChNmzZ1n33+mWhXDrx5QBxrIRqYZ3Ce5Tu+JirkWGndunWAzEydOk0cdNG+4DSL8ysmIQgO0VHs781BQz+hiAr7PfTQQ1KHCeJENND06dNljKVKlXbfffedDsE3n0ZUfHOpbKCGgCFgCCQWAn4lKo8/9phoKIhoOr9SJQklxg8FB2JIyTUdOgixgCggROQQAUQyPGTixImOTL2QD7QeS5YsEVMS22bMmOFw0v306KfSlj833HCDRPkE51tRooLPTFrSv38/V6F8eQlzTqttvG03ohJvV8TGYwgYAoZAkiDgR6KCNgWNx7Bhw934+8a7s846SyJucHrFlAPJQNOi2o/uPXoIIVmzZk3gqi5YsMDdffc9rnnz5q569eqB9Xz55uuvXalSpVyzps1kPf1AaB555JFU7fgBwWEb4c2zZs0SR97TGp1aQV4XjoVvjd/EiIrfrpiN1xAwBAyBBEHAj0SF8ONJkye5L7885u4dda8rVLCQXA2ICH4hZJslzwrS++beQiRIDBdKqI1E9tpgTQnmHvxUunXrJhqXm3vfnGp3yAbHILkcRIUFDc22bdtStfP+aNWqlatTp453lW++G1HxzaWygRoChoAhkFgI+JGocAU0coYJlJIAhAQjRNcgb731pqtXr54QiFKlS4kGhiRtwXLTTTeFNOnQjmRySkKC933ttdckTwvbq1atKvlXDh48GBhX8HH4Xb9+fQmTDrUt3tcZUYn3K2TjMwQMAUMgQRHwK1HRy6FEhYgfNC3vv/++I609BKJ06dIpmWA/+MBVqlRJUuUfOnRIdlWiQ8p7Uuh7NSo7duxwbdu2dWeemdu1b9/OVap0vit73nkOc5G2++CDD1z/fv0did6UJOmY+GQsXiHcGQ3N4MGDvat9892Iim8ulQ3UEDAEDIHEQsDvRGXQoEFiciEjLUUIVQMyYMAAR04UFXKgQBSIDsLh9q233nINGzSQ9pdeeqlkr0VrgtaDPkiPT8gyQl0fnHZZX7FixYh+KHq8X37+xYEt2W0hPSVKlJD9SafvRzGi4serZmM2BAwBQyABEPA7UaE69JVXXikaDOroDBkyxKG9CCVULkYrgrbjiy++kAKCFG2kSjJCDR5IxZw5c0JqSfCB6XVTr1QEKNRxdB3+K7Vr15ZU+jfeeKOEOus2v30aUfHbFbPxGgKGgCGQIAj4naigHcGxNdjUEi+XJ17HlV58jKikFzFrbwgYAoaAIRATBPxOVGICgnWSJgJGVNKEyBoYAoaAIWAIZAUCRlSyAtXE69OISuJdUzuDibGVAAAC+UlEQVQjQ8AQMAR8gYARFV9cphwfpBGVHL8ENgBDwBAwBJITgWQkKpnxGyGsmf01vJm7ht+Z6VPvvMz04R2P9hfu87fffks13lDjZ5233dChQ6VmUrg+g9f/IXiF/TYEDAFDwBAwBDKCQDISlZ9//llClMGLyZiU/DjkKglRwuCdwPnOdsKgaXvixIkAWeE3C+IlDN799drodu1P1zOOkydPBkgP273i7Uu38anj5py8bbz78l3PjU/GTlsdCw7JHN8r9EvOGM4XoSijtwSBt22o70ZUQqFi6wwBQ8AQMATSjUAyEpWffvrJsSjBYOJmUtb1TM5M3hAHJTWQGSU0rGMf2rOO9iy0Z9F2ur+SAN3OcXUbbfW7ftIvx1Ai4d2P9jp273fa6H70z3jYn+/0FbyPjlcx4Hy9Qn9gwr4Iie3IvButGFGJFilrZwgYAoaAIRARAYjKSy+9FLFNsmxUTUWynG96zhOisnbt2qh3MaISNVTW0BAwBAwBQyASAv369XO33Xabe/nllx2F+JJhgZhhxlixYoVbtWqVW7lypSw7d+50GzZsEOJGG7DgU7/Tjva6sP3FF1+UfmjDeiZz1m3cuNGRhXbXrl1u9erVsuh22nJ8flOZme3sx3oIAb91bLRjPdtZx+IdO32wneOR8I7jcS1ZTzsdn3es9MG5aN+6P/WLWDZt2iTbWc8CLl27dpUK0ZHuJe82IypeNOy7IWAIGAKGQIYRYAIkg+qYMWOksN7o0aOT8nPs2LGStXb8+PHu3nvvFQzARHHR77RTjHRb8O9x48a5efPmuWnTpkmf9MeifXi/s6+u1368nzoW7zrvGPQ72XQnT57sHn/8cfnUfnU/juHti9/sq+vmz58vzrKUDZg1a1ZgPdvnzp3rJk2a5LROUjQ3mxGVaFCyNoaAIWAIGAKGgCGQIwgYUckR2O2ghoAhYAgYAoaAIRANAkZUokHJ2hgChoAhYAgYAoZAjiBgRCVHYLeDGgKGgCFgCBgChkA0CPw/hcp/3zXIu+gAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. sklearn中常见的几个线性回归类库：**\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:21:36.589506Z",
     "start_time": "2020-06-16T01:21:34.243221Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_profiling as pp \n",
    "from sklearn import metrics\n",
    "# import matplotlib\n",
    "# matplotlib.use('tkAgg')\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "#全部行都能输出\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "# 解决坐标轴刻度负号乱码\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 解决中文乱码问题\n",
    "plt.rcParams['font.sans-serif'] = ['Simhei']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:21:36.939352Z",
     "start_time": "2020-06-16T01:21:36.858414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>28.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>58.255688</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>23.006936</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.9</td>\n",
       "      <td>21.6</td>\n",
       "      <td>52.263397</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>24.035009</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>48.690479</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>24.565633</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>58.239788</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>23.326177</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.4</td>\n",
       "      <td>21.9</td>\n",
       "      <td>56.174095</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>23.486480</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   station       Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  LDAPS_RHmax  \\\n",
       "0      1.0  6/30/2013          28.7          21.4    58.255688    91.116364   \n",
       "1      2.0  6/30/2013          31.9          21.6    52.263397    90.604721   \n",
       "2      3.0  6/30/2013          31.6          23.3    48.690479    83.973587   \n",
       "3      4.0  6/30/2013          32.0          23.4    58.239788    96.483688   \n",
       "4      5.0  6/30/2013          31.4          21.9    56.174095    90.155128   \n",
       "\n",
       "   LDAPS_Tmax_lapse  LDAPS_Tmin_lapse  LDAPS_WS    LDAPS_LH  ...  LDAPS_PPT2  \\\n",
       "0         28.074101         23.006936  6.818887   69.451805  ...         0.0   \n",
       "1         29.850689         24.035009  5.691890   51.937448  ...         0.0   \n",
       "2         30.091292         24.565633  6.138224   20.573050  ...         0.0   \n",
       "3         29.704629         23.326177  5.650050   65.727144  ...         0.0   \n",
       "4         29.113934         23.486480  5.735004  107.965535  ...         0.0   \n",
       "\n",
       "   LDAPS_PPT3  LDAPS_PPT4      lat      lon       DEM   Slope  \\\n",
       "0         0.0         0.0  37.6046  126.991  212.3350  2.7850   \n",
       "1         0.0         0.0  37.6046  127.032   44.7624  0.5141   \n",
       "2         0.0         0.0  37.5776  127.058   33.3068  0.2661   \n",
       "3         0.0         0.0  37.6450  127.022   45.7160  2.5348   \n",
       "4         0.0         0.0  37.5507  127.135   35.0380  0.5055   \n",
       "\n",
       "   Solar radiation  Next_Tmax  Next_Tmin  \n",
       "0      5992.895996       29.1       21.2  \n",
       "1      5869.312500       30.5       22.5  \n",
       "2      5863.555664       31.1       23.9  \n",
       "3      5856.964844       31.7       24.3  \n",
       "4      5859.552246       31.2       22.5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data = pd.read_csv(r\"Dataset.csv\")\n",
    "Data.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基本统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:45.892284Z",
     "start_time": "2020-06-16T00:12:45.810809Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <td>7750.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.211568</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Tmax</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Tmin</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>23.225059</td>\n",
       "      <td>2.413961</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>29.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>56.759372</td>\n",
       "      <td>14.668111</td>\n",
       "      <td>19.794666</td>\n",
       "      <td>45.963543</td>\n",
       "      <td>55.039024</td>\n",
       "      <td>67.190056</td>\n",
       "      <td>98.524734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>23.512589</td>\n",
       "      <td>2.345347</td>\n",
       "      <td>14.272646</td>\n",
       "      <td>22.089739</td>\n",
       "      <td>23.760199</td>\n",
       "      <td>25.152909</td>\n",
       "      <td>29.619342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.485003</td>\n",
       "      <td>1.762807</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018364</td>\n",
       "      <td>21.621661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.278200</td>\n",
       "      <td>1.161809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007896</td>\n",
       "      <td>15.841235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar radiation</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmax</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmin</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>22.932220</td>\n",
       "      <td>2.487613</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>29.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "station           7750.0    13.000000    7.211568     1.000000     7.000000   \n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "Present_Tmin      7682.0    23.225059    2.413961    11.300000    21.700000   \n",
       "LDAPS_RHmin       7677.0    56.759372   14.668111    19.794666    45.963543   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_Tmin_lapse  7677.0    23.512589    2.345347    14.272646    22.089739   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT2        7677.0     0.485003    1.762807     0.000000     0.000000   \n",
       "LDAPS_PPT3        7677.0     0.278200    1.161809     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "Next_Tmin         7725.0    22.932220    2.487613    11.300000    21.300000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "station             13.000000    19.000000    25.000000  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "Present_Tmin        23.400000    24.900000    29.900000  \n",
       "LDAPS_RHmin         55.039024    67.190056    98.524734  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_Tmin_lapse    23.760199    25.152909    29.619342  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT2           0.000000     0.018364    21.621661  \n",
       "LDAPS_PPT3           0.000000     0.007896    15.841235  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  \n",
       "Next_Tmin           23.100000    24.600000    29.800000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.describe().T "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:45.952260Z",
     "start_time": "2020-06-16T00:12:45.895274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 25 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   station           7750 non-null   float64\n",
      " 1   Date              7750 non-null   object \n",
      " 2   Present_Tmax      7682 non-null   float64\n",
      " 3   Present_Tmin      7682 non-null   float64\n",
      " 4   LDAPS_RHmin       7677 non-null   float64\n",
      " 5   LDAPS_RHmax       7677 non-null   float64\n",
      " 6   LDAPS_Tmax_lapse  7677 non-null   float64\n",
      " 7   LDAPS_Tmin_lapse  7677 non-null   float64\n",
      " 8   LDAPS_WS          7677 non-null   float64\n",
      " 9   LDAPS_LH          7677 non-null   float64\n",
      " 10  LDAPS_CC1         7677 non-null   float64\n",
      " 11  LDAPS_CC2         7677 non-null   float64\n",
      " 12  LDAPS_CC3         7677 non-null   float64\n",
      " 13  LDAPS_CC4         7677 non-null   float64\n",
      " 14  LDAPS_PPT1        7677 non-null   float64\n",
      " 15  LDAPS_PPT2        7677 non-null   float64\n",
      " 16  LDAPS_PPT3        7677 non-null   float64\n",
      " 17  LDAPS_PPT4        7677 non-null   float64\n",
      " 18  lat               7752 non-null   float64\n",
      " 19  lon               7752 non-null   float64\n",
      " 20  DEM               7752 non-null   float64\n",
      " 21  Slope             7752 non-null   float64\n",
      " 22  Solar radiation   7752 non-null   float64\n",
      " 23  Next_Tmax         7725 non-null   float64\n",
      " 24  Next_Tmin         7725 non-null   float64\n",
      "dtypes: float64(24), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "Data.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看空值（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:46.101859Z",
     "start_time": "2020-06-16T00:12:45.955239Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "station              2\n",
       "Date                 2\n",
       "Present_Tmax        70\n",
       "Present_Tmin        70\n",
       "LDAPS_RHmin         75\n",
       "LDAPS_RHmax         75\n",
       "LDAPS_Tmax_lapse    75\n",
       "LDAPS_Tmin_lapse    75\n",
       "LDAPS_WS            75\n",
       "LDAPS_LH            75\n",
       "LDAPS_CC1           75\n",
       "LDAPS_CC2           75\n",
       "LDAPS_CC3           75\n",
       "LDAPS_CC4           75\n",
       "LDAPS_PPT1          75\n",
       "LDAPS_PPT2          75\n",
       "LDAPS_PPT3          75\n",
       "LDAPS_PPT4          75\n",
       "lat                  0\n",
       "lon                  0\n",
       "DEM                  0\n",
       "Slope                0\n",
       "Solar radiation      0\n",
       "Next_Tmax           27\n",
       "Next_Tmin           27\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.isna().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:46.301511Z",
     "start_time": "2020-06-16T00:12:46.110848Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8/2/2013</td>\n",
       "      <td>29.6</td>\n",
       "      <td>25.8</td>\n",
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       "      <td>26.442801</td>\n",
       "      <td>7.980686</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>12.3700</td>\n",
       "      <td>0.0985</td>\n",
       "      <td>5357.705566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8/3/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.476059</td>\n",
       "      <td>93.672600</td>\n",
       "      <td>30.491629</td>\n",
       "      <td>25.081388</td>\n",
       "      <td>6.282423</td>\n",
       "      <td>124.191446</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>126.838</td>\n",
       "      <td>12.3700</td>\n",
       "      <td>0.0985</td>\n",
       "      <td>5332.515625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>24.0</td>\n",
       "      <td>8/11/2013</td>\n",
       "      <td>34.2</td>\n",
       "      <td>25.1</td>\n",
       "      <td>50.618851</td>\n",
       "      <td>89.166397</td>\n",
       "      <td>33.414349</td>\n",
       "      <td>27.475249</td>\n",
       "      <td>6.084058</td>\n",
       "      <td>48.521008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5237</td>\n",
       "      <td>126.909</td>\n",
       "      <td>17.2956</td>\n",
       "      <td>0.2223</td>\n",
       "      <td>5109.454102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2465</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8/5/2014</td>\n",
       "      <td>28.8</td>\n",
       "      <td>23.1</td>\n",
       "      <td>71.442566</td>\n",
       "      <td>90.695419</td>\n",
       "      <td>26.273760</td>\n",
       "      <td>23.419080</td>\n",
       "      <td>3.787889</td>\n",
       "      <td>41.739802</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010050</td>\n",
       "      <td>0.681801</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4697</td>\n",
       "      <td>126.995</td>\n",
       "      <td>82.2912</td>\n",
       "      <td>2.2579</td>\n",
       "      <td>5269.604980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3696</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7/23/2015</td>\n",
       "      <td>30.4</td>\n",
       "      <td>23.4</td>\n",
       "      <td>81.568771</td>\n",
       "      <td>93.973579</td>\n",
       "      <td>26.724457</td>\n",
       "      <td>24.984435</td>\n",
       "      <td>5.779299</td>\n",
       "      <td>58.456456</td>\n",
       "      <td>...</td>\n",
       "      <td>0.711069</td>\n",
       "      <td>0.016985</td>\n",
       "      <td>0.064332</td>\n",
       "      <td>37.5102</td>\n",
       "      <td>127.086</td>\n",
       "      <td>21.9668</td>\n",
       "      <td>0.1332</td>\n",
       "      <td>5578.788086</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3714</th>\n",
       "      <td>15.0</td>\n",
       "      <td>7/24/2015</td>\n",
       "      <td>24.6</td>\n",
       "      <td>22.4</td>\n",
       "      <td>83.265244</td>\n",
       "      <td>94.153442</td>\n",
       "      <td>26.738321</td>\n",
       "      <td>24.585545</td>\n",
       "      <td>8.048991</td>\n",
       "      <td>43.391457</td>\n",
       "      <td>...</td>\n",
       "      <td>0.803824</td>\n",
       "      <td>0.190226</td>\n",
       "      <td>0.001864</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>126.937</td>\n",
       "      <td>30.0464</td>\n",
       "      <td>0.8552</td>\n",
       "      <td>5578.187500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3739</th>\n",
       "      <td>15.0</td>\n",
       "      <td>7/25/2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.156593</td>\n",
       "      <td>93.874550</td>\n",
       "      <td>28.502294</td>\n",
       "      <td>24.270829</td>\n",
       "      <td>5.819343</td>\n",
       "      <td>35.236471</td>\n",
       "      <td>...</td>\n",
       "      <td>1.128825</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>126.937</td>\n",
       "      <td>30.0464</td>\n",
       "      <td>0.8552</td>\n",
       "      <td>5558.664551</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3886</th>\n",
       "      <td>12.0</td>\n",
       "      <td>7/31/2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.559479</td>\n",
       "      <td>86.083702</td>\n",
       "      <td>29.628166</td>\n",
       "      <td>25.745189</td>\n",
       "      <td>8.629767</td>\n",
       "      <td>26.564901</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020635</td>\n",
       "      <td>0.001050</td>\n",
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       "      <td>37.5507</td>\n",
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       "      <td>132.1180</td>\n",
       "      <td>0.5931</td>\n",
       "      <td>5477.556152</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4205</th>\n",
       "      <td>6.0</td>\n",
       "      <td>8/13/2015</td>\n",
       "      <td>30.8</td>\n",
       "      <td>23.9</td>\n",
       "      <td>35.218002</td>\n",
       "      <td>82.458534</td>\n",
       "      <td>32.241880</td>\n",
       "      <td>23.018172</td>\n",
       "      <td>6.547084</td>\n",
       "      <td>63.516243</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5102</td>\n",
       "      <td>127.042</td>\n",
       "      <td>54.6384</td>\n",
       "      <td>0.1457</td>\n",
       "      <td>5069.704102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4230</th>\n",
       "      <td>6.0</td>\n",
       "      <td>8/14/2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.412582</td>\n",
       "      <td>84.317780</td>\n",
       "      <td>32.028639</td>\n",
       "      <td>24.274533</td>\n",
       "      <td>5.880758</td>\n",
       "      <td>54.278856</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007733</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5102</td>\n",
       "      <td>127.042</td>\n",
       "      <td>54.6384</td>\n",
       "      <td>0.1457</td>\n",
       "      <td>5037.928223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4255</th>\n",
       "      <td>6.0</td>\n",
       "      <td>8/15/2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58.784390</td>\n",
       "      <td>79.509987</td>\n",
       "      <td>28.628288</td>\n",
       "      <td>24.045805</td>\n",
       "      <td>4.762958</td>\n",
       "      <td>50.826542</td>\n",
       "      <td>...</td>\n",
       "      <td>1.277620</td>\n",
       "      <td>0.002280</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5102</td>\n",
       "      <td>127.042</td>\n",
       "      <td>54.6384</td>\n",
       "      <td>0.1457</td>\n",
       "      <td>5005.629395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4280</th>\n",
       "      <td>6.0</td>\n",
       "      <td>8/16/2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.475870</td>\n",
       "      <td>89.183807</td>\n",
       "      <td>33.651696</td>\n",
       "      <td>21.381762</td>\n",
       "      <td>6.050666</td>\n",
       "      <td>61.250346</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5102</td>\n",
       "      <td>127.042</td>\n",
       "      <td>54.6384</td>\n",
       "      <td>0.1457</td>\n",
       "      <td>4972.712891</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5656</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8/9/2016</td>\n",
       "      <td>33.1</td>\n",
       "      <td>26.8</td>\n",
       "      <td>64.725151</td>\n",
       "      <td>90.769173</td>\n",
       "      <td>31.730979</td>\n",
       "      <td>26.316541</td>\n",
       "      <td>5.095636</td>\n",
       "      <td>127.601342</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>126.838</td>\n",
       "      <td>12.3700</td>\n",
       "      <td>0.0985</td>\n",
       "      <td>5140.230957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5681</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8/10/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.469501</td>\n",
       "      <td>88.160759</td>\n",
       "      <td>33.531711</td>\n",
       "      <td>27.066962</td>\n",
       "      <td>4.934146</td>\n",
       "      <td>139.353021</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>126.838</td>\n",
       "      <td>12.3700</td>\n",
       "      <td>0.0985</td>\n",
       "      <td>5110.264160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6060</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8/25/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.908253</td>\n",
       "      <td>86.309982</td>\n",
       "      <td>26.065732</td>\n",
       "      <td>21.639797</td>\n",
       "      <td>9.656940</td>\n",
       "      <td>78.263273</td>\n",
       "      <td>...</td>\n",
       "      <td>0.152258</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5372</td>\n",
       "      <td>127.085</td>\n",
       "      <td>28.7000</td>\n",
       "      <td>0.6233</td>\n",
       "      <td>4621.790527</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6268</th>\n",
       "      <td>19.0</td>\n",
       "      <td>7/2/2017</td>\n",
       "      <td>25.6</td>\n",
       "      <td>21.7</td>\n",
       "      <td>84.423805</td>\n",
       "      <td>99.652794</td>\n",
       "      <td>24.881373</td>\n",
       "      <td>22.821416</td>\n",
       "      <td>9.236040</td>\n",
       "      <td>41.559663</td>\n",
       "      <td>...</td>\n",
       "      <td>11.237815</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.025666</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>126.938</td>\n",
       "      <td>75.0924</td>\n",
       "      <td>1.7678</td>\n",
       "      <td>5882.380371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6357</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7/6/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.332268</td>\n",
       "      <td>89.395378</td>\n",
       "      <td>27.467565</td>\n",
       "      <td>24.160557</td>\n",
       "      <td>7.628294</td>\n",
       "      <td>38.808770</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.202485</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4697</td>\n",
       "      <td>126.910</td>\n",
       "      <td>52.5180</td>\n",
       "      <td>1.5629</td>\n",
       "      <td>5822.300781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6435</th>\n",
       "      <td>11.0</td>\n",
       "      <td>7/9/2017</td>\n",
       "      <td>28.8</td>\n",
       "      <td>24.5</td>\n",
       "      <td>77.014175</td>\n",
       "      <td>90.517715</td>\n",
       "      <td>28.321989</td>\n",
       "      <td>24.563500</td>\n",
       "      <td>10.349091</td>\n",
       "      <td>53.364445</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049561</td>\n",
       "      <td>0.073857</td>\n",
       "      <td>0.013500</td>\n",
       "      <td>37.5372</td>\n",
       "      <td>127.085</td>\n",
       "      <td>28.7000</td>\n",
       "      <td>0.6233</td>\n",
       "      <td>5795.268066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6907</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7/28/2017</td>\n",
       "      <td>27.5</td>\n",
       "      <td>24.3</td>\n",
       "      <td>54.392185</td>\n",
       "      <td>88.281052</td>\n",
       "      <td>30.742290</td>\n",
       "      <td>25.195872</td>\n",
       "      <td>6.311136</td>\n",
       "      <td>44.619934</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4697</td>\n",
       "      <td>126.910</td>\n",
       "      <td>52.5180</td>\n",
       "      <td>1.5629</td>\n",
       "      <td>5483.880859</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7254</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8/11/2017</td>\n",
       "      <td>30.9</td>\n",
       "      <td>23.3</td>\n",
       "      <td>51.226562</td>\n",
       "      <td>85.210350</td>\n",
       "      <td>30.742346</td>\n",
       "      <td>23.130362</td>\n",
       "      <td>5.471810</td>\n",
       "      <td>138.030061</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5117.593750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7304</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8/13/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.782753</td>\n",
       "      <td>85.662842</td>\n",
       "      <td>25.370156</td>\n",
       "      <td>21.846991</td>\n",
       "      <td>7.367599</td>\n",
       "      <td>72.823796</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038970</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001464</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5055.825195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7344</th>\n",
       "      <td>20.0</td>\n",
       "      <td>8/14/2017</td>\n",
       "      <td>26.2</td>\n",
       "      <td>22.1</td>\n",
       "      <td>85.103630</td>\n",
       "      <td>98.765335</td>\n",
       "      <td>21.925545</td>\n",
       "      <td>19.703742</td>\n",
       "      <td>14.541317</td>\n",
       "      <td>45.193849</td>\n",
       "      <td>...</td>\n",
       "      <td>3.044013</td>\n",
       "      <td>9.856375</td>\n",
       "      <td>6.913979</td>\n",
       "      <td>37.6181</td>\n",
       "      <td>127.004</td>\n",
       "      <td>146.5540</td>\n",
       "      <td>4.7296</td>\n",
       "      <td>5122.638184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7504</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8/21/2017</td>\n",
       "      <td>27.7</td>\n",
       "      <td>23.3</td>\n",
       "      <td>53.629204</td>\n",
       "      <td>94.092056</td>\n",
       "      <td>30.448380</td>\n",
       "      <td>24.038216</td>\n",
       "      <td>5.289254</td>\n",
       "      <td>114.941728</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>4786.577637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7554</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8/23/2017</td>\n",
       "      <td>30.7</td>\n",
       "      <td>23.7</td>\n",
       "      <td>78.330040</td>\n",
       "      <td>91.991310</td>\n",
       "      <td>28.959303</td>\n",
       "      <td>24.378045</td>\n",
       "      <td>12.264002</td>\n",
       "      <td>76.349382</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.064935</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>4714.059570</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7571</th>\n",
       "      <td>22.0</td>\n",
       "      <td>8/23/2017</td>\n",
       "      <td>30.2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>76.936493</td>\n",
       "      <td>90.509560</td>\n",
       "      <td>29.339601</td>\n",
       "      <td>24.659223</td>\n",
       "      <td>11.588554</td>\n",
       "      <td>78.099351</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.665117</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5102</td>\n",
       "      <td>127.086</td>\n",
       "      <td>21.9668</td>\n",
       "      <td>0.1332</td>\n",
       "      <td>4709.788086</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7604</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8/25/2017</td>\n",
       "      <td>30.3</td>\n",
       "      <td>21.7</td>\n",
       "      <td>35.563965</td>\n",
       "      <td>88.866699</td>\n",
       "      <td>28.262768</td>\n",
       "      <td>19.288583</td>\n",
       "      <td>5.651513</td>\n",
       "      <td>146.119220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>4640.095215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7682</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8/28/2017</td>\n",
       "      <td>26.3</td>\n",
       "      <td>18.1</td>\n",
       "      <td>29.959215</td>\n",
       "      <td>90.116638</td>\n",
       "      <td>23.135079</td>\n",
       "      <td>17.282587</td>\n",
       "      <td>9.292264</td>\n",
       "      <td>75.430868</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4697</td>\n",
       "      <td>126.910</td>\n",
       "      <td>52.5180</td>\n",
       "      <td>1.5629</td>\n",
       "      <td>4518.488770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      station       Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  \\\n",
       "831       7.0   8/2/2013          29.6          25.8    76.202255   \n",
       "856       7.0   8/3/2013           NaN           NaN    69.476059   \n",
       "1073     24.0  8/11/2013          34.2          25.1    50.618851   \n",
       "2465     16.0   8/5/2014          28.8          23.1    71.442566   \n",
       "3696     22.0  7/23/2015          30.4          23.4    81.568771   \n",
       "3714     15.0  7/24/2015          24.6          22.4    83.265244   \n",
       "3739     15.0  7/25/2015           NaN           NaN    68.156593   \n",
       "3886     12.0  7/31/2015           NaN           NaN    64.559479   \n",
       "4205      6.0  8/13/2015          30.8          23.9    35.218002   \n",
       "4230      6.0  8/14/2015           NaN           NaN    43.412582   \n",
       "4255      6.0  8/15/2015           NaN           NaN    58.784390   \n",
       "4280      6.0  8/16/2015           NaN           NaN    28.475870   \n",
       "5656      7.0   8/9/2016          33.1          26.8    64.725151   \n",
       "5681      7.0  8/10/2016           NaN           NaN    51.469501   \n",
       "6060     11.0  8/25/2016           NaN           NaN    44.908253   \n",
       "6268     19.0   7/2/2017          25.6          21.7    84.423805   \n",
       "6357      8.0   7/6/2017           NaN           NaN    77.332268   \n",
       "6435     11.0   7/9/2017          28.8          24.5    77.014175   \n",
       "6907      8.0  7/28/2017          27.5          24.3    54.392185   \n",
       "7254      5.0  8/11/2017          30.9          23.3    51.226562   \n",
       "7304      5.0  8/13/2017           NaN           NaN    73.782753   \n",
       "7344     20.0  8/14/2017          26.2          22.1    85.103630   \n",
       "7504      5.0  8/21/2017          27.7          23.3    53.629204   \n",
       "7554      5.0  8/23/2017          30.7          23.7    78.330040   \n",
       "7571     22.0  8/23/2017          30.2          24.9    76.936493   \n",
       "7604      5.0  8/25/2017          30.3          21.7    35.563965   \n",
       "7682      8.0  8/28/2017          26.3          18.1    29.959215   \n",
       "\n",
       "      LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_Tmin_lapse   LDAPS_WS    LDAPS_LH  \\\n",
       "831     94.058517         28.842727         26.442801   7.980686   87.715882   \n",
       "856     93.672600         30.491629         25.081388   6.282423  124.191446   \n",
       "1073    89.166397         33.414349         27.475249   6.084058   48.521008   \n",
       "2465    90.695419         26.273760         23.419080   3.787889   41.739802   \n",
       "3696    93.973579         26.724457         24.984435   5.779299   58.456456   \n",
       "3714    94.153442         26.738321         24.585545   8.048991   43.391457   \n",
       "3739    93.874550         28.502294         24.270829   5.819343   35.236471   \n",
       "3886    86.083702         29.628166         25.745189   8.629767   26.564901   \n",
       "4205    82.458534         32.241880         23.018172   6.547084   63.516243   \n",
       "4230    84.317780         32.028639         24.274533   5.880758   54.278856   \n",
       "4255    79.509987         28.628288         24.045805   4.762958   50.826542   \n",
       "4280    89.183807         33.651696         21.381762   6.050666   61.250346   \n",
       "5656    90.769173         31.730979         26.316541   5.095636  127.601342   \n",
       "5681    88.160759         33.531711         27.066962   4.934146  139.353021   \n",
       "6060    86.309982         26.065732         21.639797   9.656940   78.263273   \n",
       "6268    99.652794         24.881373         22.821416   9.236040   41.559663   \n",
       "6357    89.395378         27.467565         24.160557   7.628294   38.808770   \n",
       "6435    90.517715         28.321989         24.563500  10.349091   53.364445   \n",
       "6907    88.281052         30.742290         25.195872   6.311136   44.619934   \n",
       "7254    85.210350         30.742346         23.130362   5.471810  138.030061   \n",
       "7304    85.662842         25.370156         21.846991   7.367599   72.823796   \n",
       "7344    98.765335         21.925545         19.703742  14.541317   45.193849   \n",
       "7504    94.092056         30.448380         24.038216   5.289254  114.941728   \n",
       "7554    91.991310         28.959303         24.378045  12.264002   76.349382   \n",
       "7571    90.509560         29.339601         24.659223  11.588554   78.099351   \n",
       "7604    88.866699         28.262768         19.288583   5.651513  146.119220   \n",
       "7682    90.116638         23.135079         17.282587   9.292264   75.430868   \n",
       "\n",
       "      ...  LDAPS_PPT2  LDAPS_PPT3  LDAPS_PPT4      lat      lon       DEM  \\\n",
       "831   ...    0.000000    0.000000    0.000000  37.5776  126.838   12.3700   \n",
       "856   ...    0.000000    0.000000    0.000000  37.5776  126.838   12.3700   \n",
       "1073  ...    0.000000    0.000000    0.000000  37.5237  126.909   17.2956   \n",
       "2465  ...    0.010050    0.681801    0.000000  37.4697  126.995   82.2912   \n",
       "3696  ...    0.711069    0.016985    0.064332  37.5102  127.086   21.9668   \n",
       "3714  ...    0.803824    0.190226    0.001864  37.5507  126.937   30.0464   \n",
       "3739  ...    1.128825    0.000000    0.000000  37.5507  126.937   30.0464   \n",
       "3886  ...    0.020635    0.001050    0.000000  37.5507  126.988  132.1180   \n",
       "4205  ...    0.000000    0.000000    0.000000  37.5102  127.042   54.6384   \n",
       "4230  ...    0.000000    0.007733    0.000000  37.5102  127.042   54.6384   \n",
       "4255  ...    1.277620    0.002280    0.000000  37.5102  127.042   54.6384   \n",
       "4280  ...    0.000000    0.000000    0.000000  37.5102  127.042   54.6384   \n",
       "5656  ...    0.000000    0.000000    0.000000  37.5776  126.838   12.3700   \n",
       "5681  ...    0.008194    0.000000    0.000000  37.5776  126.838   12.3700   \n",
       "6060  ...    0.152258    0.000000    0.000000  37.5372  127.085   28.7000   \n",
       "6268  ...   11.237815    0.000000    0.025666  37.5776  126.938   75.0924   \n",
       "6357  ...    0.000000    0.202485    0.000000  37.4697  126.910   52.5180   \n",
       "6435  ...    0.049561    0.073857    0.013500  37.5372  127.085   28.7000   \n",
       "6907  ...    0.000000    0.000000    0.000000  37.4697  126.910   52.5180   \n",
       "7254  ...    0.000000    0.000000    0.000000  37.5507  127.135   35.0380   \n",
       "7304  ...    0.038970    0.000000    0.001464  37.5507  127.135   35.0380   \n",
       "7344  ...    3.044013    9.856375    6.913979  37.6181  127.004  146.5540   \n",
       "7504  ...    0.000000    0.000000    0.000000  37.5507  127.135   35.0380   \n",
       "7554  ...    0.000000    2.064935    0.000000  37.5507  127.135   35.0380   \n",
       "7571  ...    0.000000    2.665117    0.000000  37.5102  127.086   21.9668   \n",
       "7604  ...    0.000000    0.000000    0.000000  37.5507  127.135   35.0380   \n",
       "7682  ...    0.000000    0.000000    0.000000  37.4697  126.910   52.5180   \n",
       "\n",
       "       Slope  Solar radiation  Next_Tmax  Next_Tmin  \n",
       "831   0.0985      5357.705566        NaN        NaN  \n",
       "856   0.0985      5332.515625        NaN        NaN  \n",
       "1073  0.2223      5109.454102        NaN        NaN  \n",
       "2465  2.2579      5269.604980        NaN        NaN  \n",
       "3696  0.1332      5578.788086        NaN        NaN  \n",
       "3714  0.8552      5578.187500        NaN        NaN  \n",
       "3739  0.8552      5558.664551        NaN        NaN  \n",
       "3886  0.5931      5477.556152        NaN        NaN  \n",
       "4205  0.1457      5069.704102        NaN        NaN  \n",
       "4230  0.1457      5037.928223        NaN        NaN  \n",
       "4255  0.1457      5005.629395        NaN        NaN  \n",
       "4280  0.1457      4972.712891        NaN        NaN  \n",
       "5656  0.0985      5140.230957        NaN        NaN  \n",
       "5681  0.0985      5110.264160        NaN        NaN  \n",
       "6060  0.6233      4621.790527        NaN        NaN  \n",
       "6268  1.7678      5882.380371        NaN        NaN  \n",
       "6357  1.5629      5822.300781        NaN        NaN  \n",
       "6435  0.6233      5795.268066        NaN        NaN  \n",
       "6907  1.5629      5483.880859        NaN        NaN  \n",
       "7254  0.5055      5117.593750        NaN        NaN  \n",
       "7304  0.5055      5055.825195        NaN        NaN  \n",
       "7344  4.7296      5122.638184        NaN        NaN  \n",
       "7504  0.5055      4786.577637        NaN        NaN  \n",
       "7554  0.5055      4714.059570        NaN        NaN  \n",
       "7571  0.1332      4709.788086        NaN        NaN  \n",
       "7604  0.5055      4640.095215        NaN        NaN  \n",
       "7682  1.5629      4518.488770        NaN        NaN  \n",
       "\n",
       "[27 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出所有有空值的记录  （5分）\n",
    "for i in Data.columns:\n",
    "    record_isna=Data[Data[i].isna()]  \n",
    "record_isna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看每列数据的分布状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:55.448321Z",
     "start_time": "2020-06-16T00:12:46.308487Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='station', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Present_Tmax', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Present_Tmin', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_RHmin', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_RHmax', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_Tmax_lapse', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_Tmin_lapse', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_WS', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_LH', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_CC1', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_CC2', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_CC3', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_CC4', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_PPT1', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_PPT2', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_PPT3', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LDAPS_PPT4', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='lat', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='lon', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DEM', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Slope', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Solar radiation', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Next_Tmax', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Next_Tmin', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gF/iJqp71J15jIsXBy000dfbxsaUFSBjVIgBiY4R1JXnsPFePqoZd/MbN334SXwJ+DnxN1T2BrYh8w1tBVW0VkVLgLuB7qloDHPVRtnTQ8jcAb/uc72eMxkSU3n4X287UMj07mbmTw6sWcc26Ofm8fLyGC3UdzJmU5nQ4Zgz8ShKq+qeDn4vIbFXdNkz5Jt67w8kYMwZ7LtTT2t3PfSuLwvZX+C0leQDsOFdnSSJM+Xt30y+GrPplEGIxxnh09vbz5rk65k1Of3cspHA0PSeFWXmp7DhnF6/D1bA1CREpAmYBi0TkVs/qVKAv2IEZE83efKeOnj4Xdy+a4nQo43ZLSR7P2hAdYWukmsQs3J3jsj1/b8fdE/rBoEZlTBRr7uxlz4UGlk3PYkpmktPhjFvpPPcQHfsuNjodihmDYWsSqvom8KaIzFDVb01QTMZEta2nalHgzoXX9UMNS2uL80iKj2HbmavcOtc6vIab0fSTMMYE2duXGjl8pZlb5uSRnZLgdDgBkRQfy9riPLaduYrn5kgTRmyocGMmiLchwB9YVfTucv+Ai2++cIKs5HhK502ayNCC7vb5k9h25ir/8Po5JqW/14Q2+N9vQtNIF67/u6p+T0T+DXjfTwCrXRgTWD/fc5kzNW18ZlURCXH+jWbja+6JUHPH/En8T+BsTdv7koQJfSPVJH7m+fv3QY7DmKh2tbWbp7a+w21z81k4NXwG8fPXtKxkJmckcramjVtK7LpEOBn254qq1nr+Xh76mJjwjIkO3/rdKXr6Xfz9xxaFbce5kcybnEFZQwfdfQNOh2JGYdTzSYjILBGxubGNCZBXjlfzu2PVfPWOOWHdcW4k86ak41I4d7Xd6VDMKPg7wN+PgdeAJcAHgRrgj4IYlzFRoaG9h8efP8HiaZl8sbTY6XCCqignheT4WM7WtLF4WmjP0W3e42+NYJGq/gZYrarrgIIgxmRM1PjmCydp6+7n//zxUuJjI7uCHhsjlExO42xtGy67FTZs+HtW9ovID4BznlnqbFgOY8bpeGULvz9ezdfuLGFemM0VMVYLpmTQ0dPPlcZOp0MxfvI3SdwHvAV8Hfdsc58LWkTGRIHOnn5ePFLJksJMvnDrbKfDmTDzpqQTGyOcrGodubAJCf52pmsFqoCVQD8wA7A7nIwZo5dP1NDVN8B3NywhLgKbmXz130iKj2VOfhonq1pYf0P4D14YDfxNEq8DR4Br8xAq7pqFMWaIkTq4nb/azqHyJkrn5rOwIPL6RIxkYUEGZw+3UdPa7XQoxg/+/oRxqepXVPUJz2PYwf5EZJOI7BYRn1OWishkEdkx6Hm8iPzOs92DvtYZE856+108d7iCvLQEbp8fWUNv+GvB1AwEOFFpTU7hwN8ksVVEvisiC0SkyDPPhFcisgGIVdW1QIGIlHgpk427N/fgm8K/ChzwbPcREUn3sc6YsLXtTC1NnX18Yvm0iL+byZe0xDhm5adyrKLZBvwLA/6epbOBycB/B55g+GE6Snlv6tJtwDovZQZwXwwf/FNi8Ha7gRU+1r2PiDwkIgdE5EBdXd3Ql40JGY0dvew638BNRdnMzovuqTyXFmbR0NFrF7DDgL9zXP+Z59d/AdAE1A5TPBWo9Cy3AnO87K8VGDr8wNDtJvtYN3RfG4GNACtWrLCfJSZk/eepGmJi4K4ImSdiPBYVZPDCkUpePFrFDdaxLqT5O8f1N4BXgGdw/7r/t2GKtwPJnuU0f9/Dx3Zj3ZcxIaWyuYtjFS2sm5NHRnK80+E4LiUhjpJJ6bx0tAqXy37bhTJ/v3Q/qqqrgQZV3Yy7+cmXg7zXxLQUKPPzPbxtN9Z9GRNStp+9SlJ8jI2AOsjS6VlUt3Sz91KD06GYYfjdT0JEPgckichtQPMwZZ8HdohIAbAeuF9EnlRVn3c6efwMeFlEbgEWAvtwNzUNXWdMWLna1s2pqlZum5dPUnys0+GEjEUFGaQnxfHsgQrWFuc5HY7xYcSahIjcAOwFNgE3A98AfN6O6rneUOrZ5nZVPeorQahq6aDly8BdwC7gTlUd8LbOv3+WMaHjrXfqiYsV+yIcIj42ho8tLeDl49W0dttIP6Fq2CQhIn+O+1pEAfA94F9w/6K/bbjtVLVJVbeoas1oglHVKs92LcOtMyZctPf0c7SimRuLsklLtNmCh/rUiun09Lt46WiV06EYH0Y6ax8Clqpq47UVIpIFvAw8G8S4jIkIBy83MeBSVs/OdTqUkLSkMJP5U9L55d5yHri5KGInXApnIzU3xQPzRGTttQfumkRi8EMzJry5VHn7UgOz8lKZnGHzOnsjIvzp2pmcrm5lf1mT0+EYL0aqSRzBXZsY6ljgQzEmspyrbaeps48P3zDV6VBC2seXTeM7r5zhp7svcfOsHKfDMUMMmyRU9c8mKhBjIs2ximaS42NZMNVGk/Hl2mCISwszeeV4DT/cdp6c1AQeWOVz5B8zwaxzmjFB0Dfg4lR1K4sKMoiLsY/ZSNYU5xETI7z5jg2tE2rs7DUmCM7WtNHT72JJYZbToYSFzOR4VszI5tDlJpo7e50OxwxiScKYIDhW2UJaYhyz81NHLmwAuHVuPoqy3WoTIcWShDEB1tM3wNmaVm6YlkmM3dLpt+yUBFbOzOFAWSPnr7Y7HY7xsCRhTICdrmmjb0BZWmijm47WBxdMJj42hu+8fNrpUIyHJQljAuxYRTOZyfFMz0lxOpSwk5YYR+m8Sbx+5ipvnL3qdDgGSxLGBFRLZx/nattZYk1NY/aB4lzmTErj8edO0NHT73Q4Uc+ShDEB9OrJGgZU7a6mcYiLjeE7GxZT2dzF/371rNPhRD1LEsYE0EvHqshNTaAgy4bhGI+VM3P4r2tm8NPdZWw7M9xEmCbYLEkYEyD17T3sOl/PksJMG6guAB69ZwELpmbw37Ycpbqly+lwopaNXWxMgLxyvBqXMqqmpmvDUpjrJcXH8sMHlvPRp3fytWeOsPkvVhEXe/3vWm/H0Ib1CByrSRgTIC8drWbe5HQb8TWAivPTePITN/B2WSP/8Po5p8OJSkFJEiKySUR2i4jPKUuHlhGRL4nIds/jiIj8s4jEiUj5oPWLgxGvMeNV3dLF22WNfHSpjfgaaBtuLOSPbyrk6W3n2XrKrk9MtIAnCRHZAMSq6lqgQERK/Cmjqj9W1VLPlKY7gI3AEuCZa+tV9Xig4zUmEH5/rBqAjywpcDiSyPS/PnEDi6dl8jf/cYQLddYbeyIFoyZRCmzxLG8D1o2mjIhMAyar6kFgNXCviOwUkV+JyHXXUETkIRE5ICIH6upszBfjjJeOVrGkMJOZeTZWUzAkxcey/oYpDKhy/z/v5V93XmLzvnK7pjMBgpEkUoFKz3IrMHmUZb4M/NizvB+4TVXXAc3APUN3pKobVXWFqq7Iz88ff/TGjNLlhg6OVrTwUatFBFVWSgKfvrmIho4efn2wApeq0yFFhWAkiXYg2bOc5uM9vJYRkRjgdlV9w/PaMVWt9iyfAa5rujLGab/zNDX9lyV2PSLYivPTWH/DVE5Vt9rcExMkGEniIO81Hy0FykZR5hZg36ByvxCRpSISC9wLHA10sMaM10tHq1g5M5uCrOSRC5txW1ucy7LpWbx2qpZztW1OhxPxgpEkngf+RES+D3wKOCkiT45Q5vee9XcDbw0q9y3gF7jn2t6jqq8FIV5jxuyd2jbO1LTx0aXW1DRRRIRPLJtGfnoivz5YQbuN7xRUAU8SqtqK+8L0XtxNR0dV9fERyrR41v8PVf3toHInVHWJqi5W1ccCHasx4/W7o1XECKy/wZqaJlJCXAz3rZxOV98Azx2qQO36RNAEpZ+Eqjap6hZVrRlPGWNCmary0rFq1hbnkZ+e6HQ4UWdqZjIfWjiZ0zVtHK9scTqciGU9ro0Zo0PlzVyq7+Bjy6ypySlr5+QxLSuZl45V09lrzU7BYEnCmDH6zaEKkuNjuWexNTU5JUaEDTdOo6u3n9dOW2/sYLAB/owZg+6+AV46WsX6G6aQlmgfo0AbTSe5qZnJrJyZw9uXGlk9O5dJ6TZ2ViBZTcKYMdh6qpa27n4+eVOh06EY3psb+w8n7BJnoFmSMGYMfn2wgoLMJNbMznU6FIN7buzb5uZzpqaNK42dTocTUSxJGOOna2MF/WT7Bd56p465U9KJibHJhULFmtm5JMfH8sbZq06HElEsSRgzSkeuNKPAjUXZTodiBkmMj2VdSR5nato4YbfEBowlCWNGQVU5WN5EUU4KeWnWNyLUrJmdS1J8DD9584LToUQMSxLGjEJZQyd1bT3cNMNqEaEoKT6WFTNyeOVEDVXNNi92INi9e8aMwp6LDSTFx7DUM4+1zWcQetYU57LrfD2P/OYYHx40XIrNez02VpMwxk8tXX2cqmphxYwcEuLsoxOqslMSWDQtk7fLGuntdzkdTtizM90YP+271IAqrJqV43QoZgSrZ+fQ3efiZJVdwB4vSxLG+KGjp599FxuZPzWDXLtgHfJm5aaSm5rAgctNTocS9ixJGOOH/9h/ha6+AW4ryXM6FOMHEWHFjGwu1XdQ39bjdDhhzZKEMSPoG3CxaeclZuamUJSb6nQ4xk/LZ2QTI1htYpwsSRgzgi0HrlDZ3MVtc/OdDsWMQkZSPPMmp3O4vIkBl01KNFZBSRIisklEdovI4/6WEZE4ESkXke2ex2LP+idEZL+I/DAYsRoznO6+Af7x9XPcNCObuZPTnQ7HjNKKmTm09fRztsbmwh6rgCcJEdkAxKrqWqBAREr8LLMEeEZVSz2P4yKyAlgH3AxUiMidgY7XmOH8cu9lalt7+Prd8xCxcZrCzdzJ6aQnxXHgcqPToYStYNQkSoEtnuVtuL/k/SmzGrhXRHaKyK9EJA64FfiNuiewfQ24ZeiOROQhETkgIgfq6uoC+g8x0a2tu49/euM8t5TksdpGew1LsTHCjUXZnK1p42prt9PhhKVgJIlUoNKz3ApM9rPMfuA2VV0HNAP3+LMvVd2oqitUdUV+vrUZm8D5151lNHX28bcfmud0KGYclhdlocALR6qcDiUsBWNYjnYg2bOchvdE5K3MMVW9dq/aGaDEz30ZE1Cb95XT3tPPj7afZ+HUDE5WtXKyqtXpsMwYTUpPojA7md8eruQvbp3tdDhhJxhfugd5r4lpKVDmZ5lfiMhSEYkF7gWO+rkvYwJu66ka+gZc3LXQW0XYhJtl07M4Xd3K6WpL9qMVjJrE88AOESkA1gP3i8iTqvr4MGVWA8eAzYAAL6rqayISA3xHRP4B+LDnYUxQVTR1cqCsiQ/MyWNyhs2XHAmWFGbxhxM1PHe4kgVTM5wOJ6wEvCahqq24L0zvBW5X1aNDEoS3Mi2qekJVl6jqYlV9zFPOBdwJ7ADWq+qlQMdrzGAul/LS0SpSE+O4Y/4kp8MxAZKWGEfpvHxeOFJpfSZGKSht/KrapKpbVNXnrOT+lPGU61LVX6vqxcBHasz7/fpQBVeauvjwDVNIio91OhwTQPcuL6S2tYfdF+qdDiWs2IVgYzxauvr43h/OUJSTwrLpWU6HYwLsgwsmkZ4Ux3OHKkcubN5lScIYj++8fJqmzj4+trSAGOs4F3GS4mP5yJKpvHKiho6efqfDCRuWJIwBdp2v59/3X+EvbplNQVbyyBuYsHTv8kK6+gZ49eSwrdxmEEsSJup19vbz6G+PMysvlb+687pRZEwEWTEjm8LsZJ47bE1O/rIkYaLe9//zHcobO/nOhsV2sTrCxcQI9y6fxq7z9dS02DAd/rAkYaLawctN/OuuS3xmVZGNzxQl7l0+DZfCC0esNuEPSxImarX39PPX/3GEqZnJPLJ+vtPhmAkyOz+NZdOzrMnJT5YkTNR64sWTVDR18oP7l5GeFO90OGYCbbhxGmdq2jhlY3KNyJKEiUqvHK/m2YMVPFw6h5Uzc5wOx0ywjywpID5WeO5whdOhhDxLEibqVLd08chvj7O0MJOv2d1MUSknNYHSeZN4/kgV/QMup8MJacEY4M+YkLR5Xzn9Ay7+ZcdFuvoG+OD8ycTH2u+kaLVh+TS2nqpl14UGm798GJYkTFR56Vg1V5q6eODmIvLSE9m8r9zpkIxD7lgwiYykOJ47VGFJYhiWJEzU2F/WyP6yRm4tyeeGaZlOh2MmmLcfBPOnZPDqyVo6evpJTbSvQ2+srm2iwqHyJl48WsWc/DQ+tMgmEjJuy4uy6Oob4A8nbJgOXyxJmIh3/mobD/50P5nJ8dy3croN3mfeVZSTQlFOCs8evOJ0KCHLkoSJaNUtXXxu09vExcTwZ2tnWpOCeR8R4f6bp7P3YiPnr7Y5HU5ICkqSEJFNIrJbRB73t4yIZIrIKyKyVUSeE5EEEYkTkXIR2e55LA5GvCaybN5XzuZ95fzfHRf5+A930dDRy/0rp5Obluh0aCYEfWrFdBJiY/jlXruJwZuAJwkR2QDEqupaoEBErrsR3UeZzwDfV9W7gBrc81kvAZ5R1VLP43ig4zWRqb2nn007L9HQ0ctnV8+w4b+NT3lpiaxfPIXfHKygs9fmmRgqGDWJUmCLZ3kbsM6fMqr6I1Xd6lmXD1wFVgP3ishOEfmViFzXViAiD4nIARE5UFdXF8B/hglXLV19bHzrIvXtPXxuzQyK89OcDsmEsM37ypmSkURbTz+P/faE3RY9RDCSRCpwbeSsVsDbrSQ+y4jIGiBbVfcC+4HbVHUd0AzcM3RHqrpRVVeo6or8fLvXOdqV1Xew8a0LtHX38adrZ1EyKd3pkEwYKMpJYUpGEnsvNaCqTocTUoKRJNqBa3X7NB/v4bWMiOQATwMPel47pqrVnuUzgI2hYHzae7GBT/xoF919Lj6/bhaz8lKdDsmECRFh1ewcqlu6udLY6XQ4ISUYSeIg7zUxLQXK/CkjIgm4m6AeVdXLntd+ISJLRSQWuBc4GoR4TQT497fL+ez/3UduagIPlxZTmJ3idEgmzCybnkViXAx7LzU6HUpICcb9gM8DO0SkAFgP3C8iT6rq48OUWQ18HrgJeExEHgN+DHwL2AwI8KKqvhaEeE0Y+/nuMl4+Uc3ei42UTErj/pVFJCfY7HJm9BLjYrlxRjb7LjZQ2dzFNLvZAQAJRvubiGQDdwFvqarXroz+lBmtFStW6IEDBwKxKxMGLjd08Ol/2UtVczfr5uRx96IpxMZYRzkzds2dvfyf/zzLf107k7/76CKnw5kwInJQVVd4ey0o/SRUtUlVtwz35e9PGWN8efFoFR/5x500dvTy2VUzuGfxVEsQZtyyUhJYNj2Lf3/7Co0dvU6HExKsx7UJK40dvXz5V4f4y2cOM2dyGl+9vYSFBRlOh2UiyK0l+XT3D/DPb11wOpSQYEnChAVV5eXj1XzoqTf5z1M1fP3ueTz7hTVkpyY4HZqJMJMykvjEsmn8dFcZta3dTofjOEsSJuS9U9vGZzft4+FfHWJSehIvfmUdX759DnE2YZAJkr++cy4DLuUfXj/ndCiOs9HOTMiqau7iR9vP88zbV0hLjONbH1/EAzcXWXIwQVeUm8JnV8/g53vK+MyqIhYVRO/8I5YkTMgpb+jkx2+e59cH3ZPUf/rm6fzNXfPIsaYlM4H++s65vHS0im++cJJnv7CGmCi9McKShHHUtXFyVJULdR3suVDPmdo24mNiuH9lEV8sLWZaVrKNp2MmXGZKPI+sn8/Xf32MzW+X89nVM5wOyRGWJIyjevtdHL7SxJ4LDVxt6yE1IZbSufmsmpVLRnI8b561QRuNcz55YyEvHKni278/zdriXGZH4WCRliSMI640dvLLvZf5+Z7LdPUNUJCZxB/dWMjiwkzi7ZqDcdjgmusH5uRx8HITX/v3Izz7xTUkxUdXj35LEmbCqCp7Lzby092X2HqqFhFhwdQMPlCcS1FOCmLTipoQlJkczydvLOSX+y7zyG+O8dR9y6LqXLUkYYKuqaOXl45VsXlfOWdq2shOieeLtxXz2dUz2G7NSSYMLCzI4Ot3z+N/v3qWgqxkvn73vKhJFJYkTFC09/Tz1jt1vHS0itdO19I3oCycmsH3PrmEjy0riLoquwl/D5cWU9HUyY+2XyA2Rvibu+ZGRaKwJGECQlW5VN/BzvP1vHHmKrvON9A74CI3NYE/WT2TlIRYCrKS6Xcpvz1UOfIOjQkxIsK3P7GYAZfy9LbzVLd08//du5iEuMi+hmZJwoxZQ3sP33v1LBeutnP+ajvNXX0A5KQmsHJmNgsKMpiRk2oD75mIERMj/P+fXEJBVjI/eO0cZ2vaeOq+ZcyZFLl3PVmSMH5r7Ojl0OUm9l9uZOe5ek5WtQKQFB9DcX4at87Np2RSGjmpCVFRDTfRSUT4qzvnsmBqBo/85hj3/OMOHrplNl8qLSY1MfK+UoMyn4RTbD6JwGns6OWd2jbO1bZx5EoL289epcEzdHKsCNNzUpgzKY2SSWkUZCVbbcFEpdbuPs5Ut/L8kSqyUuL53OoZfGrl9LCbGXG4+SQsSUQ4VaWzd4Cmzl6aO/to6uylqbOPFs9fb+sbO3pp7e5/dx+5qQlMykiiKCeFopwUCrOTrS+DMYNcaexk+9mrnK5pA2BpYSZr5+SxtjiXJYVZZCbHOxzh8CY8SYjIJmAB8LKqPulvGX/X+eJUklBVBlzKgCouF/S7XLgUYgRiY4QYkXf/xgh+NcWoKv0upaffRXffAD39Lnr6Bmjv6aetu5/Wrj733+4+Woc8b+nqo/ndL/0+egdcPt8nPTGOzJR4slMSyPL8zU6JpzA7hZLJacybks6UjCSeeftKIA+ZMRGpqaOXw1eaaenq5XB5M/0u9/drdko8M3JTKchKIsvzGXN/5hJIS4wlNTGOlIQ4UhNjSU2IIyXBvS4xLmZCmm6HSxIBb0ATkQ1ArKquFZEfiUiJqp4bqQyw2J91Q/cVCGdqWvnq5sO4VHEp7i98l7q//FUZcDFoWXF5SQij4St5XGuy6e5z0dM/MKr9JsbFkBwfS5LnkZIQy4ycFFKmuE+4a4/khMHP44ZtJqpq7qaq2cbTN8Zf2akJ3DF/EgD3LJ7K5YZOalu7aWjvpb6jh8pLXXT29tPV59/nO0YgNSGOuFghNiaG2Bh3c29MjPu749qyAKXz8nnsvywM+L8pGFdZSoEtnuVtwDpg6Be7tzLL/Vw3NOE8BDzkedouImfHGHceUD/GbaOBHZ+R2TEanh2fkY35GL0GPD729/U5emEwkkQqcO1G+FZgjp9l/F33Pqq6Edg43qBF5ICv6pax4+MPO0bDs+MzslA8RsG4+tgOJHuW03y8h7cy/q4zxhgzQYLxpXsQd7MQwFKgzM8y/q4zxhgzQYLR3PQ8sENECoD1wP0i8qSqPj5MmdWA+rkuWMbdZBXh7PiMzI7R8Oz4jCzkjlGwboHNBu4C3lLVGn/L+LvOGGPMxIioznTGGGMCyy4EG2OMQ0QkR0TuEpE8p2PxJaqThIhMFpEdnuV4EfmdiOwWkQedji0UDDk+00SkQkS2ex75TsfnJBHJFJFXRGSriDwnIgkisslz/ozjdvXI4eMYlQ86hxY7HaOTRGQq8HvgZuANEckPxXMoapOE51rHz3D3xQD4KnBAVdcCHxGRdMeCCwFejs8q4NuqWup5RPuUcp8Bvq+qdwE1wP14RgcACjwjBkS7ocfoEeCZQefQcWfDc9wi4K9V9dvAq8AdhOA5FLVJAhgA7sPdSQ/e3wt8NxBSHVocMPT4rAYeFpE9IvKUc2GFBlX9kapu9TzNBz7L9aMDRDUvx6gfuFdEdorIr0Qk8sbVHgVVfU1V94rIrbhrE3cTgudQ1CYJVW1V1ZZBq4b27p488VGFDi/H5xVgraquAeaKyBKHQgspIrIGyAauYOePV4OO0VbgNlVdBzQD9zgZVygQ9+h99wF9gBCC51DUJgkvrHf38Haraptn+QwQElVhJ4lIDvA08CB2/ng15BgdU9Vqz0t2DgHq9mXcrRerCcFzKCSCCBHWu3t4r4rIVBFJwV0tPuF0QE4SkQTcTQOPqupl7Py5jpdj9AsRWSoiscC9wFFHA3SYiHxDRD7neZoFfJcQPIeiuk1wiJ8BL4vILcBCYJ/D8YSaJ4A3gF7gJ6o61tF2I8XngZuAx0TkMeDfgD+ZoNEBwsXQY/QG8AvczSovquprTgYXAjYCW0Tkz3H/6HoeeCvUziHrTDeI5z9nHfDqkPZ4Y0ZkowOY8QrFc8iShDHGGJ/smoQxxhifLEkYY4zxyZKEMcYYnyxJGGOM8cmShDFDiMhPReQ7nuW/F5G/H8W2y0Rk2QhlvuIZ4K7L8/feYcpOEZFH/H1/YwLNkoQx3v25iCSNYbtlnodPqvpDVS0FKj0D3T03TNkaVf3uGOIwJiAsSRjj3Qnco5gCpIjIr0XkLRH5JwARWSciz4pIjGfQw+me2scjwCMi8vpo3kxEDnqG1X5BRPaJyF951s8UkZ8OKvdTEfmmiOzwDCmd7GufxgSCJQljvPsn4Aue5YeAE6p6KzBVRJao6k7c4zX9EHheVa+o6qO4h1b4rqp+cJTvlwL8MbAEeAD3sAy+pKnqLcARYPko38eYUbFhOYzxrgb3IHSlwDO4h7guxT3GzjTgGO5Esgf3MNjjVauq7SJyGfcw7TJM2Z95/l4FEgLw3sb4ZDUJY3x7CrgN+DTwA891hMeBcs/rjwPfAf7noG26cNcKrg0DHQwdQdqvMdexJGGMD6p6GHgT+BdgvYi8BXwRuCIifwRUqeo3gUUicqNns63ABhHZBdziRNzGBJKN3WSMMcYnuyZhTBCJyPYhq1pU9eNOxGLMWFhNwhhjjE92TcIYY4xPliSMMcb4ZEnCGGOMT5YkjDHG+PT/AB7CcX1PviOcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制每列数据的直方图（分布频次）\n",
    "plot = Data.drop('Date',axis=1) # 删除'Date'列\n",
    "\n",
    "for I in plot.columns:\n",
    "    sns.distplot(plot[I]) \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成数据概况报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:21.940528Z",
     "start_time": "2020-06-16T00:12:55.451319Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# # 生成整体数据概要\n",
    "# data_report=pp.ProfileReport(Data) \n",
    "# data_report\n",
    "\n",
    "# # 也可以把生成的报告导出保存\n",
    "# data_report.to_file('report.html') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:33:52.629282Z",
     "start_time": "2020-06-16T01:33:52.621285Z"
    }
   },
   "outputs": [],
   "source": [
    "# 由于本数据集的特殊性（有两个样本标签：次日最高气温和最低气温）\n",
    "#，本案例中我们只进行次日最高温预测--因此这里只选出与最高温预测相关的变量，作为建模要用的数据\n",
    "\n",
    "Max = Data[['Present_Tmax','LDAPS_RHmax','LDAPS_Tmax_lapse','LDAPS_WS','LDAPS_LH','LDAPS_CC1','LDAPS_CC2','LDAPS_CC3','LDAPS_CC4','LDAPS_PPT1','LDAPS_PPT4',\n",
    "          'lat','lon','DEM','Slope','Solar radiation','Next_Tmax']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:33:56.575804Z",
     "start_time": "2020-06-16T01:33:56.551818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.7</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>0.233947</td>\n",
       "      <td>0.203896</td>\n",
       "      <td>0.161697</td>\n",
       "      <td>0.130928</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.9</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>0.225508</td>\n",
       "      <td>0.251771</td>\n",
       "      <td>0.159444</td>\n",
       "      <td>0.127727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.6</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>0.209344</td>\n",
       "      <td>0.257469</td>\n",
       "      <td>0.204091</td>\n",
       "      <td>0.142125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>0.216372</td>\n",
       "      <td>0.226002</td>\n",
       "      <td>0.161157</td>\n",
       "      <td>0.134249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.4</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>0.151407</td>\n",
       "      <td>0.249995</td>\n",
       "      <td>0.178892</td>\n",
       "      <td>0.170021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS    LDAPS_LH  \\\n",
       "0          28.7    91.116364         28.074101  6.818887   69.451805   \n",
       "1          31.9    90.604721         29.850689  5.691890   51.937448   \n",
       "2          31.6    83.973587         30.091292  6.138224   20.573050   \n",
       "3          32.0    96.483688         29.704629  5.650050   65.727144   \n",
       "4          31.4    90.155128         29.113934  5.735004  107.965535   \n",
       "\n",
       "   LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0   0.233947   0.203896   0.161697   0.130928         0.0         0.0   \n",
       "1   0.225508   0.251771   0.159444   0.127727         0.0         0.0   \n",
       "2   0.209344   0.257469   0.204091   0.142125         0.0         0.0   \n",
       "3   0.216372   0.226002   0.161157   0.134249         0.0         0.0   \n",
       "4   0.151407   0.249995   0.178892   0.170021         0.0         0.0   \n",
       "\n",
       "       lat      lon       DEM   Slope  Solar radiation  Next_Tmax  \n",
       "0  37.6046  126.991  212.3350  2.7850      5992.895996       29.1  \n",
       "1  37.6046  127.032   44.7624  0.5141      5869.312500       30.5  \n",
       "2  37.5776  127.058   33.3068  0.2661      5863.555664       31.1  \n",
       "3  37.6450  127.022   45.7160  2.5348      5856.964844       31.7  \n",
       "4  37.5507  127.135   35.0380  0.5055      5859.552246       31.2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:22.290321Z",
     "start_time": "2020-06-16T00:15:22.130413Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 17 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Present_Tmax      7682 non-null   float64\n",
      " 1   LDAPS_RHmax       7677 non-null   float64\n",
      " 2   LDAPS_Tmax_lapse  7677 non-null   float64\n",
      " 3   LDAPS_WS          7677 non-null   float64\n",
      " 4   LDAPS_LH          7677 non-null   float64\n",
      " 5   LDAPS_CC1         7677 non-null   float64\n",
      " 6   LDAPS_CC2         7677 non-null   float64\n",
      " 7   LDAPS_CC3         7677 non-null   float64\n",
      " 8   LDAPS_CC4         7677 non-null   float64\n",
      " 9   LDAPS_PPT1        7677 non-null   float64\n",
      " 10  LDAPS_PPT4        7677 non-null   float64\n",
      " 11  lat               7752 non-null   float64\n",
      " 12  lon               7752 non-null   float64\n",
      " 13  DEM               7752 non-null   float64\n",
      " 14  Slope             7752 non-null   float64\n",
      " 15  Solar radiation   7752 non-null   float64\n",
      " 16  Next_Tmax         7725 non-null   float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "Max.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:22.570160Z",
     "start_time": "2020-06-16T00:15:22.293320Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Present_Tmax</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar radiation</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmax</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 填充缺失值（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:07.620687Z",
     "start_time": "2020-06-16T01:34:07.603697Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Present_Tmax', 'LDAPS_RHmax', 'LDAPS_Tmax_lapse', 'LDAPS_WS',\n",
       "       'LDAPS_LH', 'LDAPS_CC1', 'LDAPS_CC2', 'LDAPS_CC3', 'LDAPS_CC4',\n",
       "       'LDAPS_PPT1', 'LDAPS_PPT4', 'lat', 'lon', 'DEM', 'Slope',\n",
       "       'Solar radiation', 'Next_Tmax'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Present_Tmax          29.768211\n",
       "LDAPS_RHmax           88.374804\n",
       "LDAPS_Tmax_lapse      29.613447\n",
       "LDAPS_WS               7.097875\n",
       "LDAPS_LH              62.505019\n",
       "LDAPS_CC1              0.368774\n",
       "LDAPS_CC2              0.356080\n",
       "LDAPS_CC3              0.318404\n",
       "LDAPS_CC4              0.299191\n",
       "LDAPS_PPT1             0.591995\n",
       "LDAPS_PPT4             0.269407\n",
       "lat                   37.544722\n",
       "lon                  126.991397\n",
       "DEM                   61.867972\n",
       "Slope                  1.257048\n",
       "Solar radiation     5341.502803\n",
       "Next_Tmax             30.274887\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.7</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>0.233947</td>\n",
       "      <td>0.203896</td>\n",
       "      <td>0.161697</td>\n",
       "      <td>0.130928</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.9</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>0.225508</td>\n",
       "      <td>0.251771</td>\n",
       "      <td>0.159444</td>\n",
       "      <td>0.127727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.514100</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.6</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>0.209344</td>\n",
       "      <td>0.257469</td>\n",
       "      <td>0.204091</td>\n",
       "      <td>0.142125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.266100</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>0.216372</td>\n",
       "      <td>0.226002</td>\n",
       "      <td>0.161157</td>\n",
       "      <td>0.134249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.534800</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.4</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>0.151407</td>\n",
       "      <td>0.249995</td>\n",
       "      <td>0.178892</td>\n",
       "      <td>0.170021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.505500</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>23.3</td>\n",
       "      <td>78.869858</td>\n",
       "      <td>26.352081</td>\n",
       "      <td>6.148918</td>\n",
       "      <td>72.058294</td>\n",
       "      <td>0.030034</td>\n",
       "      <td>0.081035</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5372</td>\n",
       "      <td>126.891</td>\n",
       "      <td>15.5876</td>\n",
       "      <td>0.155400</td>\n",
       "      <td>4443.313965</td>\n",
       "      <td>28.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>23.3</td>\n",
       "      <td>77.294975</td>\n",
       "      <td>27.010193</td>\n",
       "      <td>6.542819</td>\n",
       "      <td>47.241457</td>\n",
       "      <td>0.035874</td>\n",
       "      <td>0.074962</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5237</td>\n",
       "      <td>126.909</td>\n",
       "      <td>17.2956</td>\n",
       "      <td>0.222300</td>\n",
       "      <td>4438.373535</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>23.2</td>\n",
       "      <td>77.243744</td>\n",
       "      <td>27.939516</td>\n",
       "      <td>7.289264</td>\n",
       "      <td>9.090034</td>\n",
       "      <td>0.048954</td>\n",
       "      <td>0.059869</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000796</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5237</td>\n",
       "      <td>126.970</td>\n",
       "      <td>19.5844</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>4451.345215</td>\n",
       "      <td>27.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>20.0</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4562</td>\n",
       "      <td>126.826</td>\n",
       "      <td>12.3700</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>37.6</td>\n",
       "      <td>100.000153</td>\n",
       "      <td>38.542255</td>\n",
       "      <td>21.857621</td>\n",
       "      <td>213.414006</td>\n",
       "      <td>0.967277</td>\n",
       "      <td>0.968353</td>\n",
       "      <td>0.983789</td>\n",
       "      <td>0.974710</td>\n",
       "      <td>23.701544</td>\n",
       "      <td>16.655469</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.135</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>5.178230</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>38.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7752 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse   LDAPS_WS    LDAPS_LH  \\\n",
       "0             28.7    91.116364         28.074101   6.818887   69.451805   \n",
       "1             31.9    90.604721         29.850689   5.691890   51.937448   \n",
       "2             31.6    83.973587         30.091292   6.138224   20.573050   \n",
       "3             32.0    96.483688         29.704629   5.650050   65.727144   \n",
       "4             31.4    90.155128         29.113934   5.735004  107.965535   \n",
       "...            ...          ...               ...        ...         ...   \n",
       "7747          23.3    78.869858         26.352081   6.148918   72.058294   \n",
       "7748          23.3    77.294975         27.010193   6.542819   47.241457   \n",
       "7749          23.2    77.243744         27.939516   7.289264    9.090034   \n",
       "7750          20.0    58.936283         17.624954   2.882580  -13.603212   \n",
       "7751          37.6   100.000153         38.542255  21.857621  213.414006   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0      0.233947   0.203896   0.161697   0.130928    0.000000    0.000000   \n",
       "1      0.225508   0.251771   0.159444   0.127727    0.000000    0.000000   \n",
       "2      0.209344   0.257469   0.204091   0.142125    0.000000    0.000000   \n",
       "3      0.216372   0.226002   0.161157   0.134249    0.000000    0.000000   \n",
       "4      0.151407   0.249995   0.178892   0.170021    0.000000    0.000000   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7747   0.030034   0.081035   0.000000   0.000000    0.000000    0.000000   \n",
       "7748   0.035874   0.074962   0.000000   0.000000    0.000000    0.000000   \n",
       "7749   0.048954   0.059869   0.000000   0.000796    0.000000    0.000000   \n",
       "7750   0.000000   0.000000   0.000000   0.000000    0.000000    0.000000   \n",
       "7751   0.967277   0.968353   0.983789   0.974710   23.701544   16.655469   \n",
       "\n",
       "          lat      lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "0     37.6046  126.991  212.3350  2.785000      5992.895996       29.1  \n",
       "1     37.6046  127.032   44.7624  0.514100      5869.312500       30.5  \n",
       "2     37.5776  127.058   33.3068  0.266100      5863.555664       31.1  \n",
       "3     37.6450  127.022   45.7160  2.534800      5856.964844       31.7  \n",
       "4     37.5507  127.135   35.0380  0.505500      5859.552246       31.2  \n",
       "...       ...      ...       ...       ...              ...        ...  \n",
       "7747  37.5372  126.891   15.5876  0.155400      4443.313965       28.3  \n",
       "7748  37.5237  126.909   17.2956  0.222300      4438.373535       28.6  \n",
       "7749  37.5237  126.970   19.5844  0.271300      4451.345215       27.8  \n",
       "7750  37.4562  126.826   12.3700  0.098475      4329.520508       17.4  \n",
       "7751  37.6450  127.135  212.3350  5.178230      5992.895996       38.9  \n",
       "\n",
       "[7752 rows x 17 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用每列的均值填充其缺失值\n",
    "Max.columns\n",
    "Max[Max.columns].mean()\n",
    "dic = dict(zip(Max[Max.columns].mean().index,Max[Max.columns].mean()))\n",
    "Max = Max.fillna(dic)\n",
    "Max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:10.245846Z",
     "start_time": "2020-06-16T01:34:10.235840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Present_Tmax        0\n",
       "LDAPS_RHmax         0\n",
       "LDAPS_Tmax_lapse    0\n",
       "LDAPS_WS            0\n",
       "LDAPS_LH            0\n",
       "LDAPS_CC1           0\n",
       "LDAPS_CC2           0\n",
       "LDAPS_CC3           0\n",
       "LDAPS_CC4           0\n",
       "LDAPS_PPT1          0\n",
       "LDAPS_PPT4          0\n",
       "lat                 0\n",
       "lon                 0\n",
       "DEM                 0\n",
       "Slope               0\n",
       "Solar radiation     0\n",
       "Next_Tmax           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标准化处理连续型特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:15:24.140777Z",
     "start_time": "2020-06-16T02:15:24.099800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>-0.376282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "      <td>0.072097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "      <td>0.264260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "      <td>0.456422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "      <td>0.296287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  LDAPS_CC1  \\\n",
       "0     -0.361326     0.383078         -0.524889 -0.128382  0.206966  -0.516243   \n",
       "1      0.721084     0.311586          0.080895 -0.646994 -0.314841  -0.548557   \n",
       "2      0.619608    -0.614982          0.162936 -0.441604 -1.249283  -0.610450   \n",
       "3      0.754909     1.133054          0.031092 -0.666247  0.095997  -0.583539   \n",
       "4      0.551957     0.248765         -0.170325 -0.627154  1.354409  -0.832287   \n",
       "\n",
       "   LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4       lat  \\\n",
       "0  -0.592636  -0.629013  -0.664815    -0.30575   -0.224453  1.189286   \n",
       "1  -0.406199  -0.638055  -0.677462    -0.30575   -0.224453  1.189286   \n",
       "2  -0.384009  -0.458843  -0.620575    -0.30575   -0.224453  0.653021   \n",
       "3  -0.506548  -0.631178  -0.651696    -0.30575   -0.224453  1.991696   \n",
       "4  -0.413115  -0.559990  -0.510358    -0.30575   -0.224453  0.118743   \n",
       "\n",
       "        lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "0 -0.005000  2.772243  1.115004         1.517935  -0.376282  \n",
       "1  0.511177 -0.315157 -0.542158         1.229950   0.072097  \n",
       "2  0.838510 -0.526218 -0.723133         1.216534   0.264260  \n",
       "3  0.385280 -0.297588  0.932424         1.201176   0.456422  \n",
       "4  1.807917 -0.494322 -0.548433         1.207205   0.296287  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#标准化处理连续型特征 （10分）\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "\n",
    "Scaled_Data = StandardScaler().fit_transform(Max) \n",
    "print(type(Scaled_Data))\n",
    "Scaled_Data=pd.DataFrame(Scaled_Data,columns=Max.columns) \n",
    "Scaled_Data.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分特征列和标签列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:50.260685Z",
     "start_time": "2020-06-16T01:34:50.234704Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  LDAPS_CC1  \\\n",
       "0     -0.361326     0.383078         -0.524889 -0.128382  0.206966  -0.516243   \n",
       "1      0.721084     0.311586          0.080895 -0.646994 -0.314841  -0.548557   \n",
       "2      0.619608    -0.614982          0.162936 -0.441604 -1.249283  -0.610450   \n",
       "3      0.754909     1.133054          0.031092 -0.666247  0.095997  -0.583539   \n",
       "4      0.551957     0.248765         -0.170325 -0.627154  1.354409  -0.832287   \n",
       "\n",
       "   LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4       lat  \\\n",
       "0  -0.592636  -0.629013  -0.664815    -0.30575   -0.224453  1.189286   \n",
       "1  -0.406199  -0.638055  -0.677462    -0.30575   -0.224453  1.189286   \n",
       "2  -0.384009  -0.458843  -0.620575    -0.30575   -0.224453  0.653021   \n",
       "3  -0.506548  -0.631178  -0.651696    -0.30575   -0.224453  1.991696   \n",
       "4  -0.413115  -0.559990  -0.510358    -0.30575   -0.224453  0.118743   \n",
       "\n",
       "        lon       DEM     Slope  Solar radiation  \n",
       "0 -0.005000  2.772243  1.115004         1.517935  \n",
       "1  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3  0.385280 -0.297588  0.932424         1.201176  \n",
       "4  1.807917 -0.494322 -0.548433         1.207205  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Scaled_Data.drop(['Next_Tmax'],axis=1) \n",
    "X.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:09.014361Z",
     "start_time": "2020-06-16T01:35:09.005367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7750   -4.123453\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7752, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = Scaled_Data['Next_Tmax']\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 创建线性回归模型并评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分数据集（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:15.723572Z",
     "start_time": "2020-06-16T01:35:15.714578Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,Y,test_size=0.3,random_state=100)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:18.264310Z",
     "start_time": "2020-06-16T01:35:18.247275Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score,cross_val_predict\n",
    "lr = LinearRegression().fit(Xtrain,Ytrain)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:32:40.607647Z",
     "start_time": "2020-06-15T06:32:40.603648Z"
    }
   },
   "source": [
    "## 评估（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:23.925342Z",
     "start_time": "2020-06-16T01:35:23.911350Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7551196741909566, 0.7522855025647004)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(Xtrain,Ytrain),lr.score(Xtest,Ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算MSE与RMSE（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:24.025817Z",
     "start_time": "2020-06-16T01:22:24.016823Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.24820246817581793, 0.4981992253866097)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "y_pred =lr.predict(Xtest)#得到预测结果\n",
    "# 评估训练集集合情况  参数1:真实标签 参数2:预测标签\n",
    "MSE = mean_squared_error(Ytest,y_pred)\n",
    "RMSE = np.sqrt(MSE)\n",
    "MSE,RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型诊断"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制线性拟合图（10分）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:24.480812Z",
     "start_time": "2020-06-16T00:15:24.344885Z"
    }
   },
   "source": [
    "'''\n",
    "从上可以看到RMSE(越小越好）的值0.5，我们通过可视化来看一下训练后的预测和真实值之间的差异：\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:30.193284Z",
     "start_time": "2020-06-16T01:22:29.785111Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2173d44b508>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2173c1aab08>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2173d6973c8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画个折线图来看看模型拟合的好坏程度\n",
    "plt.figure(figsize=(20,10)) \n",
    "plt.plot(range(len(Ytest)), Ytest, 'r', label='Y测试值')\n",
    "plt.plot(range(len(Ytest)), y_pred, 'b', label='Y预测值')\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:25.123437Z",
     "start_time": "2020-06-16T00:15:25.118441Z"
    }
   },
   "source": [
    "'''\n",
    "可以看到许多地方，红线会明显超出蓝线，说明我们的模型拟合度不够，再换个散点图来更直观地看一下：\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:33.969981Z",
     "start_time": "2020-06-16T01:22:33.602723Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2173c041688>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2173d427408>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Y测试值')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y预测值')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用散点图观测（10分）\n",
    "plt.scatter(Ytest, y_pred)\n",
    "plt.plot([Ytest.min(),Ytest.max()],[Ytest.min(),Ytest.max()]) \n",
    "plt.xlabel('Y测试值')\n",
    "plt.ylabel('Y预测值') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:25.593168Z",
     "start_time": "2020-06-16T00:15:25.588171Z"
    }
   },
   "source": [
    "'''\n",
    "图中我们可以看到，如果完全拟合，散点应该和直线相重合，这里发现，y_test有一些的异常值，\n",
    "而线性回归模型的一大缺点就是对异常值很敏感，会极大影响模型的准确性，因此，下一步，我们就根据这一点，对模型进行优化\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制残差图（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:26.237802Z",
     "start_time": "2020-06-16T00:15:25.595167Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2173c127ac8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2173c222d88>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Y_observed')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y_predicted')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制残差图查看模型的拟合情况（10分）\n",
    "from sklearn.model_selection import cross_val_predict,cross_val_score\n",
    "Y_pred=cross_val_predict(lr,X,Y,cv=10)\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(20,10)) \n",
    "ax.scatter(Y,Y-Y_pred) \n",
    "ax.axhline(lw=8,color='blue')\n",
    "ax.set_xlabel('Y_observed')\n",
    "ax.set_ylabel('Y_predicted')\n",
    "plt.show()  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制相关性热图（10分）\n",
    "\n",
    "**绘制各预测变量间的相关性热图，查看是否存在相关性很高的变量（共线性或近似共线性）**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.379205Z",
     "start_time": "2020-06-16T00:15:26.240804Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMax=Scaled_Data.corr()   # 经过测试，使用标准化前后的数据得到的相关系数是一样的\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(15,10))\n",
    "mask=np.array(corrMax)\n",
    "mask[np.tril_indices_from(mask)]=False\n",
    "sns.heatmap(corrMax,mask=mask, vmax=1,square=True,annot=True,cmap='Blues') \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除异常值（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:34.870734Z",
     "start_time": "2020-06-16T01:35:34.674966Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####================寻找异常值====================######\n",
    "\n",
    "Scaled_Data['Next_Tmax'].plot.box() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:28:18.424085Z",
     "start_time": "2020-06-16T02:28:18.411090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.662770694023558"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "-3.8575878207889565"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除掉Next_Tmax中小于下边缘值的记录（10分）\n",
    "Q1,Q3=np.percentile(Scaled_Data['Next_Tmax'],(0.25,0.75) )\n",
    "up_whisker=Q3+1.5*(Q3-Q1)\n",
    "low_whisker=Q1-1.5*(Q3-Q1)\n",
    "up_whisker\n",
    "low_whisker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:16:00.616309Z",
     "start_time": "2020-06-16T02:16:00.579332Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6175</th>\n",
       "      <td>-2.695272</td>\n",
       "      <td>1.457979</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>4.911091</td>\n",
       "      <td>-0.210420</td>\n",
       "      <td>1.489658</td>\n",
       "      <td>2.281600</td>\n",
       "      <td>2.656311</td>\n",
       "      <td>2.268567</td>\n",
       "      <td>-0.017489</td>\n",
       "      <td>1.340116</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.786106</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>-3.304127</td>\n",
       "      <td>-4.113443</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>-1.939757</td>\n",
       "      <td>-2.267499</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.386643</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845455</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "6175     -2.695272     1.457979         -4.087857  4.911091 -0.210420   \n",
       "7750     -3.304127    -4.113443         -4.087857 -1.939757 -2.267499   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "6175   1.489658   2.281600   2.656311   2.268567   -0.017489    1.340116   \n",
       "7750  -1.412018  -1.386643  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "6175  1.189286 -0.005000  2.772243  1.115004        -1.786106  -4.123453  \n",
       "7750 -1.758184 -2.082302 -0.911963 -0.845455        -2.358212  -4.123453  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Y中小于箱型图下边缘的值\n",
    "Scaled_Data[Scaled_Data['Next_Tmax']<low_whisker] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:24:40.455480Z",
     "start_time": "2020-06-16T02:24:40.448484Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6175, 7750], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要删除的样本的Index\n",
    "drop_index=Scaled_Data[Scaled_Data['Next_Tmax']<low_whisker].index.values\n",
    "drop_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:25:42.036217Z",
     "start_time": "2020-06-16T02:25:41.961262Z"
    }
   },
   "outputs": [],
   "source": [
    "# 删除Y中小于箱型图下边缘的值\n",
    "X=X.drop(drop_index)\n",
    "Y=Y.drop(drop_index) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:25:54.069979Z",
     "start_time": "2020-06-16T02:25:54.035982Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7750 rows x 16 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7750, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除后的X,Y -------少了两行\n",
    "X\n",
    "Y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:21:08.371115Z",
     "start_time": "2020-06-16T03:21:08.337137Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.22952887192574337\n",
      "RMSE: 0.4790917155678476\n"
     ]
    }
   ],
   "source": [
    "# 重新划分数据集\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1)\n",
    "# 对训练集进行训练\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, Y_train) \n",
    "# 对测试集进行预测\n",
    "Y_pred = lr.predict(X_test)\n",
    "MSE = metrics.mean_squared_error(Y_test, Y_pred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.726012Z",
     "start_time": "2020-06-16T00:15:27.652048Z"
    }
   },
   "outputs": [],
   "source": [
    "# 上一次评估结果\n",
    "# MSE: 0.25266554606897074\n",
    "# RMSE: 0.502658478560713  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.917423Z",
     "start_time": "2020-06-16T00:15:27.728011Z"
    }
   },
   "source": [
    "'''\n",
    "可以看到，该模型的RMSE由原来的0.5降低到了0.48，小幅度的提升了模型的准确率;去除异常值，提高准确度。\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:27.527723Z",
     "start_time": "2020-06-16T03:22:27.496745Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7750 rows x 16 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:30.962802Z",
     "start_time": "2020-06-16T03:22:30.906836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征筛选，选出和样本标签之间存在显著相关性（p<0.05)的特征，从而过滤掉对模型预测贡献不大的特征\n",
    "from sklearn.feature_selection import f_regression\n",
    "\n",
    "F,p=f_regression(X,Y)  # 一个特征对应一个F值和p值\n",
    "k=F.shape[0] - (p>0.05).sum() # 去掉p值<=0.05的特征，k就是剩下的特征数量\n",
    "k "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:33.930084Z",
     "start_time": "2020-06-16T03:22:33.914094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7750, 14)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.36132577,  0.38307796, -0.52488856, ...,  1.18928568,\n",
       "         2.77224286,  1.11500407],\n",
       "       [ 0.72108401,  0.31158619,  0.08089519, ...,  1.18928568,\n",
       "        -0.31515742, -0.54215762],\n",
       "       [ 0.61960809, -0.61498177,  0.16293647, ...,  0.65302103,\n",
       "        -0.52621832, -0.7231326 ],\n",
       "       ...,\n",
       "       [-2.18789227, -1.54818379, -0.88766178, ..., -0.41752209,\n",
       "        -0.82121274, -0.75509512],\n",
       "       [-2.22171758, -1.55534234, -0.57077984, ..., -0.41752209,\n",
       "        -0.77904331, -0.71933797],\n",
       "       [ 2.64912642,  1.62440928,  3.04456067, ...,  1.99169648,\n",
       "         2.77224286,  2.86143459]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# F检验筛选特征\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "X_f=SelectKBest(f_regression,k=k).fit_transform(X,Y) \n",
    "X_f.shape    # 过滤掉了两个特征\n",
    "X_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:38.343703Z",
     "start_time": "2020-06-16T03:22:38.336692Z"
    }
   },
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(X_f,Y,test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:40.510995Z",
     "start_time": "2020-06-16T03:22:40.504012Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(xtrain,ytrain) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:45.574369Z",
     "start_time": "2020-06-16T03:22:45.562377Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.745518217297051, 0.7594863753705541)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试集上的评分（R²）\n",
    "lr.score(xtrain,ytrain),lr.score(xtest,ytest) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:50.126024Z",
     "start_time": "2020-06-16T03:22:50.117031Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.23443534187388365\n",
      "RMSE: 0.4841852350845528\n"
     ]
    }
   ],
   "source": [
    "ypred = lr.predict(xtest) \n",
    "MSE = metrics.mean_squared_error(ytest, ypred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(ytest, ypred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA降维\n",
    "\n",
    "主成分分析的目的是从现有的特征中重建新的特征，新的特征剔除了原有特征中的冗余信息，因此更有区分度。\n",
    "\n",
    "主成分分析的结果是得到新的特征，而不是简单地舍弃原来的特征列表中的一些特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:58.610715Z",
     "start_time": "2020-06-16T03:22:58.587725Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca=PCA(n_components=0.97).fit(X,Y) \n",
    "X_pca=pca.transform(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:04.873414Z",
     "start_time": "2020-06-16T03:23:04.832436Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.216577</td>\n",
       "      <td>3.103277</td>\n",
       "      <td>-0.110833</td>\n",
       "      <td>0.165686</td>\n",
       "      <td>0.790641</td>\n",
       "      <td>-0.681205</td>\n",
       "      <td>-1.317347</td>\n",
       "      <td>-0.238028</td>\n",
       "      <td>-0.203376</td>\n",
       "      <td>0.603971</td>\n",
       "      <td>-0.337200</td>\n",
       "      <td>0.358007</td>\n",
       "      <td>-0.423250</td>\n",
       "      <td>1.099815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.090162</td>\n",
       "      <td>-0.107501</td>\n",
       "      <td>1.262260</td>\n",
       "      <td>0.415918</td>\n",
       "      <td>0.528656</td>\n",
       "      <td>-0.648804</td>\n",
       "      <td>-1.089290</td>\n",
       "      <td>-0.077244</td>\n",
       "      <td>-0.327796</td>\n",
       "      <td>0.455522</td>\n",
       "      <td>0.200883</td>\n",
       "      <td>-0.248300</td>\n",
       "      <td>-0.819938</td>\n",
       "      <td>0.253945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.170936</td>\n",
       "      <td>-0.890398</td>\n",
       "      <td>0.501055</td>\n",
       "      <td>0.423675</td>\n",
       "      <td>0.775087</td>\n",
       "      <td>-1.372693</td>\n",
       "      <td>-0.972450</td>\n",
       "      <td>0.227718</td>\n",
       "      <td>0.223689</td>\n",
       "      <td>0.190134</td>\n",
       "      <td>0.099512</td>\n",
       "      <td>-0.201590</td>\n",
       "      <td>-0.922498</td>\n",
       "      <td>0.184477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.834243</td>\n",
       "      <td>1.217238</td>\n",
       "      <td>1.663132</td>\n",
       "      <td>0.726037</td>\n",
       "      <td>0.587636</td>\n",
       "      <td>-0.459220</td>\n",
       "      <td>-0.996600</td>\n",
       "      <td>-0.280367</td>\n",
       "      <td>-0.815869</td>\n",
       "      <td>0.995221</td>\n",
       "      <td>0.419671</td>\n",
       "      <td>-0.486277</td>\n",
       "      <td>-0.841180</td>\n",
       "      <td>-0.672248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.214938</td>\n",
       "      <td>0.125820</td>\n",
       "      <td>1.699884</td>\n",
       "      <td>0.871620</td>\n",
       "      <td>0.306261</td>\n",
       "      <td>0.069075</td>\n",
       "      <td>-0.711520</td>\n",
       "      <td>-0.439632</td>\n",
       "      <td>-0.165456</td>\n",
       "      <td>-1.432835</td>\n",
       "      <td>-0.681152</td>\n",
       "      <td>-0.127686</td>\n",
       "      <td>-0.932863</td>\n",
       "      <td>0.190224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7745</th>\n",
       "      <td>-1.786437</td>\n",
       "      <td>0.231800</td>\n",
       "      <td>-0.034922</td>\n",
       "      <td>0.032411</td>\n",
       "      <td>-3.623162</td>\n",
       "      <td>-0.618894</td>\n",
       "      <td>0.170518</td>\n",
       "      <td>0.229131</td>\n",
       "      <td>1.534717</td>\n",
       "      <td>-1.291132</td>\n",
       "      <td>-0.853182</td>\n",
       "      <td>-0.036252</td>\n",
       "      <td>-0.137269</td>\n",
       "      <td>-0.045560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-1.871831</td>\n",
       "      <td>-0.153308</td>\n",
       "      <td>-1.028628</td>\n",
       "      <td>-0.810918</td>\n",
       "      <td>-3.635659</td>\n",
       "      <td>0.140778</td>\n",
       "      <td>-0.260850</td>\n",
       "      <td>0.833126</td>\n",
       "      <td>0.884765</td>\n",
       "      <td>0.511711</td>\n",
       "      <td>-0.727532</td>\n",
       "      <td>0.529375</td>\n",
       "      <td>-0.279357</td>\n",
       "      <td>-0.127452</td>\n",
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       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-1.904300</td>\n",
       "      <td>-0.383570</td>\n",
       "      <td>-1.415379</td>\n",
       "      <td>-0.856730</td>\n",
       "      <td>-3.348666</td>\n",
       "      <td>-0.376563</td>\n",
       "      <td>-0.161274</td>\n",
       "      <td>0.946401</td>\n",
       "      <td>1.242176</td>\n",
       "      <td>0.431955</td>\n",
       "      <td>-0.515940</td>\n",
       "      <td>0.329307</td>\n",
       "      <td>-0.214308</td>\n",
       "      <td>-0.169483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-1.829787</td>\n",
       "      <td>-0.559760</td>\n",
       "      <td>-1.535874</td>\n",
       "      <td>-0.621879</td>\n",
       "      <td>-2.943078</td>\n",
       "      <td>-1.374214</td>\n",
       "      <td>-0.052400</td>\n",
       "      <td>1.204855</td>\n",
       "      <td>1.792964</td>\n",
       "      <td>0.294714</td>\n",
       "      <td>-0.109837</td>\n",
       "      <td>-0.169241</td>\n",
       "      <td>-0.115426</td>\n",
       "      <td>-0.188458</td>\n",
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       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>8.725825</td>\n",
       "      <td>3.056087</td>\n",
       "      <td>4.609578</td>\n",
       "      <td>4.595430</td>\n",
       "      <td>8.330166</td>\n",
       "      <td>9.435757</td>\n",
       "      <td>5.962135</td>\n",
       "      <td>0.194339</td>\n",
       "      <td>10.953023</td>\n",
       "      <td>3.910947</td>\n",
       "      <td>0.153466</td>\n",
       "      <td>1.686319</td>\n",
       "      <td>-0.332968</td>\n",
       "      <td>0.823091</td>\n",
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       "</table>\n",
       "<p>7750 rows × 14 columns</p>\n",
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       "            0         1         2         3         4         5         6   \\\n",
       "0    -0.216577  3.103277 -0.110833  0.165686  0.790641 -0.681205 -1.317347   \n",
       "1    -1.090162 -0.107501  1.262260  0.415918  0.528656 -0.648804 -1.089290   \n",
       "2    -1.170936 -0.890398  0.501055  0.423675  0.775087 -1.372693 -0.972450   \n",
       "3    -0.834243  1.217238  1.663132  0.726037  0.587636 -0.459220 -0.996600   \n",
       "4    -1.214938  0.125820  1.699884  0.871620  0.306261  0.069075 -0.711520   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "7745 -1.786437  0.231800 -0.034922  0.032411 -3.623162 -0.618894  0.170518   \n",
       "7746 -1.871831 -0.153308 -1.028628 -0.810918 -3.635659  0.140778 -0.260850   \n",
       "7747 -1.904300 -0.383570 -1.415379 -0.856730 -3.348666 -0.376563 -0.161274   \n",
       "7748 -1.829787 -0.559760 -1.535874 -0.621879 -2.943078 -1.374214 -0.052400   \n",
       "7749  8.725825  3.056087  4.609578  4.595430  8.330166  9.435757  5.962135   \n",
       "\n",
       "            7          8         9         10        11        12        13  \n",
       "0    -0.238028  -0.203376  0.603971 -0.337200  0.358007 -0.423250  1.099815  \n",
       "1    -0.077244  -0.327796  0.455522  0.200883 -0.248300 -0.819938  0.253945  \n",
       "2     0.227718   0.223689  0.190134  0.099512 -0.201590 -0.922498  0.184477  \n",
       "3    -0.280367  -0.815869  0.995221  0.419671 -0.486277 -0.841180 -0.672248  \n",
       "4    -0.439632  -0.165456 -1.432835 -0.681152 -0.127686 -0.932863  0.190224  \n",
       "...        ...        ...       ...       ...       ...       ...       ...  \n",
       "7745  0.229131   1.534717 -1.291132 -0.853182 -0.036252 -0.137269 -0.045560  \n",
       "7746  0.833126   0.884765  0.511711 -0.727532  0.529375 -0.279357 -0.127452  \n",
       "7747  0.946401   1.242176  0.431955 -0.515940  0.329307 -0.214308 -0.169483  \n",
       "7748  1.204855   1.792964  0.294714 -0.109837 -0.169241 -0.115426 -0.188458  \n",
       "7749  0.194339  10.953023  3.910947  0.153466  1.686319 -0.332968  0.823091  \n",
       "\n",
       "[7750 rows x 14 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca=pd.DataFrame(X_pca)\n",
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:07.297511Z",
     "start_time": "2020-06-16T03:23:07.288516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
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       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7750, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:10.305510Z",
     "start_time": "2020-06-16T03:23:10.277525Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "      <th>5</th>\n",
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       "      <th>7</th>\n",
       "      <th>8</th>\n",
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       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
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       "      <th>15</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.228159</td>\n",
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       "      <td>0.069054</td>\n",
       "      <td>0.108524</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.177762</td>\n",
       "      <td>0.259298</td>\n",
       "      <td>-0.115402</td>\n",
       "      <td>0.139626</td>\n",
       "      <td>0.253892</td>\n",
       "      <td>-0.080516</td>\n",
       "      <td>-0.148532</td>\n",
       "      <td>-0.188108</td>\n",
       "      <td>-0.188080</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>-0.060552</td>\n",
       "      <td>0.175085</td>\n",
       "      <td>0.061905</td>\n",
       "      <td>0.573748</td>\n",
       "      <td>0.580675</td>\n",
       "      <td>0.008013</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.041856</td>\n",
       "      <td>0.375808</td>\n",
       "      <td>0.133046</td>\n",
       "      <td>-0.096357</td>\n",
       "      <td>0.346909</td>\n",
       "      <td>0.204690</td>\n",
       "      <td>0.055840</td>\n",
       "      <td>-0.139376</td>\n",
       "      <td>-0.192081</td>\n",
       "      <td>0.314158</td>\n",
       "      <td>-0.091826</td>\n",
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       "      <td>0.361806</td>\n",
       "      <td>-0.282789</td>\n",
       "      <td>-0.231722</td>\n",
       "      <td>0.160499</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.128714</td>\n",
       "      <td>-0.109038</td>\n",
       "      <td>-0.004835</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.086365</td>\n",
       "      <td>-0.269335</td>\n",
       "      <td>-0.083357</td>\n",
       "      <td>0.229271</td>\n",
       "      <td>0.328601</td>\n",
       "      <td>-0.366989</td>\n",
       "      <td>0.459058</td>\n",
       "      <td>0.392905</td>\n",
       "      <td>0.459881</td>\n",
       "      <td>0.010425</td>\n",
       "      <td>0.048139</td>\n",
       "      <td>-0.099735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.521499</td>\n",
       "      <td>-0.132052</td>\n",
       "      <td>0.415585</td>\n",
       "      <td>0.200768</td>\n",
       "      <td>-0.109847</td>\n",
       "      <td>0.064168</td>\n",
       "      <td>0.077508</td>\n",
       "      <td>0.086216</td>\n",
       "      <td>0.099563</td>\n",
       "      <td>0.174102</td>\n",
       "      <td>0.008157</td>\n",
       "      <td>-0.070494</td>\n",
       "      <td>0.084456</td>\n",
       "      <td>0.188751</td>\n",
       "      <td>0.208160</td>\n",
       "      <td>0.580179</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.234427</td>\n",
       "      <td>0.185108</td>\n",
       "      <td>-0.007308</td>\n",
       "      <td>0.228682</td>\n",
       "      <td>0.630832</td>\n",
       "      <td>-0.071018</td>\n",
       "      <td>-0.105731</td>\n",
       "      <td>-0.007234</td>\n",
       "      <td>0.102064</td>\n",
       "      <td>0.046327</td>\n",
       "      <td>0.409067</td>\n",
       "      <td>-0.213486</td>\n",
       "      <td>-0.445201</td>\n",
       "      <td>-0.111096</td>\n",
       "      <td>-0.084715</td>\n",
       "      <td>-0.031756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.377125</td>\n",
       "      <td>-0.085739</td>\n",
       "      <td>0.078499</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>0.043436</td>\n",
       "      <td>0.071895</td>\n",
       "      <td>0.110212</td>\n",
       "      <td>0.108762</td>\n",
       "      <td>0.049060</td>\n",
       "      <td>0.444529</td>\n",
       "      <td>-0.093587</td>\n",
       "      <td>-0.110817</td>\n",
       "      <td>0.207857</td>\n",
       "      <td>0.087366</td>\n",
       "      <td>0.163401</td>\n",
       "      <td>-0.717383</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.013714</td>\n",
       "      <td>-0.274986</td>\n",
       "      <td>-0.115457</td>\n",
       "      <td>0.835690</td>\n",
       "      <td>-0.034486</td>\n",
       "      <td>-0.002497</td>\n",
       "      <td>-0.048047</td>\n",
       "      <td>-0.071086</td>\n",
       "      <td>-0.092055</td>\n",
       "      <td>-0.053476</td>\n",
       "      <td>-0.223025</td>\n",
       "      <td>0.311199</td>\n",
       "      <td>-0.028485</td>\n",
       "      <td>-0.119224</td>\n",
       "      <td>-0.142026</td>\n",
       "      <td>-0.103987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.370291</td>\n",
       "      <td>-0.141150</td>\n",
       "      <td>0.121759</td>\n",
       "      <td>0.177218</td>\n",
       "      <td>-0.215333</td>\n",
       "      <td>-0.003833</td>\n",
       "      <td>-0.205263</td>\n",
       "      <td>-0.251031</td>\n",
       "      <td>-0.129045</td>\n",
       "      <td>0.434367</td>\n",
       "      <td>0.584006</td>\n",
       "      <td>-0.194281</td>\n",
       "      <td>0.238188</td>\n",
       "      <td>0.003956</td>\n",
       "      <td>-0.061626</td>\n",
       "      <td>0.022683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.042747</td>\n",
       "      <td>-0.017327</td>\n",
       "      <td>0.098680</td>\n",
       "      <td>-0.178946</td>\n",
       "      <td>-0.295307</td>\n",
       "      <td>-0.123886</td>\n",
       "      <td>-0.067586</td>\n",
       "      <td>0.010266</td>\n",
       "      <td>0.076458</td>\n",
       "      <td>0.310972</td>\n",
       "      <td>0.161651</td>\n",
       "      <td>0.616572</td>\n",
       "      <td>-0.574300</td>\n",
       "      <td>0.036589</td>\n",
       "      <td>0.062666</td>\n",
       "      <td>-0.070417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.228455</td>\n",
       "      <td>0.371271</td>\n",
       "      <td>0.185369</td>\n",
       "      <td>0.123466</td>\n",
       "      <td>-0.328695</td>\n",
       "      <td>0.234673</td>\n",
       "      <td>0.281510</td>\n",
       "      <td>-0.140353</td>\n",
       "      <td>-0.417383</td>\n",
       "      <td>-0.404841</td>\n",
       "      <td>0.328316</td>\n",
       "      <td>0.009849</td>\n",
       "      <td>-0.060252</td>\n",
       "      <td>-0.034421</td>\n",
       "      <td>0.054214</td>\n",
       "      <td>-0.211079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.034273</td>\n",
       "      <td>-0.637297</td>\n",
       "      <td>-0.063050</td>\n",
       "      <td>-0.208273</td>\n",
       "      <td>0.323071</td>\n",
       "      <td>0.465238</td>\n",
       "      <td>0.257615</td>\n",
       "      <td>-0.075044</td>\n",
       "      <td>-0.237589</td>\n",
       "      <td>-0.093466</td>\n",
       "      <td>0.184750</td>\n",
       "      <td>0.156335</td>\n",
       "      <td>-0.079597</td>\n",
       "      <td>0.142051</td>\n",
       "      <td>0.039583</td>\n",
       "      <td>0.025827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.352815</td>\n",
       "      <td>0.057000</td>\n",
       "      <td>0.671374</td>\n",
       "      <td>0.083665</td>\n",
       "      <td>0.102315</td>\n",
       "      <td>0.319565</td>\n",
       "      <td>-0.077398</td>\n",
       "      <td>-0.062434</td>\n",
       "      <td>0.416761</td>\n",
       "      <td>-0.181899</td>\n",
       "      <td>-0.123503</td>\n",
       "      <td>0.028882</td>\n",
       "      <td>-0.074246</td>\n",
       "      <td>0.140435</td>\n",
       "      <td>-0.075031</td>\n",
       "      <td>-0.195759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.048350</td>\n",
       "      <td>0.064477</td>\n",
       "      <td>0.013980</td>\n",
       "      <td>0.004915</td>\n",
       "      <td>0.015767</td>\n",
       "      <td>-0.187600</td>\n",
       "      <td>0.140001</td>\n",
       "      <td>0.123015</td>\n",
       "      <td>-0.138519</td>\n",
       "      <td>0.039964</td>\n",
       "      <td>0.012322</td>\n",
       "      <td>0.013607</td>\n",
       "      <td>0.012477</td>\n",
       "      <td>0.666108</td>\n",
       "      <td>-0.676660</td>\n",
       "      <td>-0.016238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "          0         1         2         3         4         5         6   \\\n",
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       "1  -0.177762  0.259298 -0.115402  0.139626  0.253892 -0.080516 -0.148532   \n",
       "2   0.041856  0.375808  0.133046 -0.096357  0.346909  0.204690  0.055840   \n",
       "3   0.128714 -0.109038 -0.004835 -0.019345  0.086365 -0.269335 -0.083357   \n",
       "4   0.521499 -0.132052  0.415585  0.200768 -0.109847  0.064168  0.077508   \n",
       "5   0.234427  0.185108 -0.007308  0.228682  0.630832 -0.071018 -0.105731   \n",
       "6   0.377125 -0.085739  0.078499  0.002232  0.043436  0.071895  0.110212   \n",
       "7   0.013714 -0.274986 -0.115457  0.835690 -0.034486 -0.002497 -0.048047   \n",
       "8  -0.370291 -0.141150  0.121759  0.177218 -0.215333 -0.003833 -0.205263   \n",
       "9   0.042747 -0.017327  0.098680 -0.178946 -0.295307 -0.123886 -0.067586   \n",
       "10  0.228455  0.371271  0.185369  0.123466 -0.328695  0.234673  0.281510   \n",
       "11 -0.034273 -0.637297 -0.063050 -0.208273  0.323071  0.465238  0.257615   \n",
       "12 -0.352815  0.057000  0.671374  0.083665  0.102315  0.319565 -0.077398   \n",
       "13  0.048350  0.064477  0.013980  0.004915  0.015767 -0.187600  0.140001   \n",
       "\n",
       "          7         8         9         10        11        12        13  \\\n",
       "0   0.400378  0.343841  0.192053  0.162411  0.025265 -0.008065  0.076506   \n",
       "1  -0.188108 -0.188080  0.000994 -0.060552  0.175085  0.061905  0.573748   \n",
       "2  -0.139376 -0.192081  0.314158 -0.091826  0.459231  0.361806 -0.282789   \n",
       "3   0.229271  0.328601 -0.366989  0.459058  0.392905  0.459881  0.010425   \n",
       "4   0.086216  0.099563  0.174102  0.008157 -0.070494  0.084456  0.188751   \n",
       "5  -0.007234  0.102064  0.046327  0.409067 -0.213486 -0.445201 -0.111096   \n",
       "6   0.108762  0.049060  0.444529 -0.093587 -0.110817  0.207857  0.087366   \n",
       "7  -0.071086 -0.092055 -0.053476 -0.223025  0.311199 -0.028485 -0.119224   \n",
       "8  -0.251031 -0.129045  0.434367  0.584006 -0.194281  0.238188  0.003956   \n",
       "9   0.010266  0.076458  0.310972  0.161651  0.616572 -0.574300  0.036589   \n",
       "10 -0.140353 -0.417383 -0.404841  0.328316  0.009849 -0.060252 -0.034421   \n",
       "11 -0.075044 -0.237589 -0.093466  0.184750  0.156335 -0.079597  0.142051   \n",
       "12 -0.062434  0.416761 -0.181899 -0.123503  0.028882 -0.074246  0.140435   \n",
       "13  0.123015 -0.138519  0.039964  0.012322  0.013607  0.012477  0.666108   \n",
       "\n",
       "          14        15  \n",
       "0   0.069054  0.108524  \n",
       "1   0.580675  0.008013  \n",
       "2  -0.231722  0.160499  \n",
       "3   0.048139 -0.099735  \n",
       "4   0.208160  0.580179  \n",
       "5  -0.084715 -0.031756  \n",
       "6   0.163401 -0.717383  \n",
       "7  -0.142026 -0.103987  \n",
       "8  -0.061626  0.022683  \n",
       "9   0.062666 -0.070417  \n",
       "10  0.054214 -0.211079  \n",
       "11  0.039583  0.025827  \n",
       "12 -0.075031 -0.195759  \n",
       "13 -0.676660 -0.016238  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "components=pca.components_   #14个新特征的特征向量(每一行) \n",
    "components=pd.DataFrame(components) \n",
    "components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:51.506451Z",
     "start_time": "2020-06-16T03:23:51.497454Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25542858, 0.12862968, 0.08850255, 0.08191585, 0.07267401,\n",
       "       0.06801911, 0.05608564, 0.05187105, 0.05015169, 0.04070604,\n",
       "       0.0339251 , 0.02304823, 0.01785588, 0.01306852])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_ #主成分的解释方差百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:54.442229Z",
     "start_time": "2020-06-16T03:23:54.432222Z"
    }
   },
   "outputs": [],
   "source": [
    "# 重新划分训练集和测试集\n",
    "Xtrain,Xtest,Ytrain,Ytest=train_test_split(X_pca,Y, test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:57.857425Z",
     "start_time": "2020-06-16T03:23:57.848431Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(Xtrain,Ytrain) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:59.855182Z",
     "start_time": "2020-06-16T03:23:59.839193Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7383072663535954, 0.7383072663535954)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型在训练集和测试机上的评分（R²）\n",
    "lr.score(Xtrain,Ytrain),lr.score(Xtrain,Ytrain)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 随机森林回归 RandomForestRegression（20分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:36:37.843504Z",
     "start_time": "2020-06-16T00:15:30.474184Z"
    }
   },
   "outputs": [],
   "source": [
    "# 1.使用集成算法-随机森林回归器\n",
    "# 2.使用网格搜索寻找主要参数的最优取值组合\n",
    "# 3.使用去除异常值后的训练集数据训练模型（15分）\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "rfr=RandomForestRegressor()  # 观察题目需求，RandomForestRegressor()参数均为默认值。 \n",
    "\n",
    "Search_CV={'n_estimators':range(1,200,10),'max_depth':range(1,10)}   #根据题目需求确定最优参数的查询范围\n",
    "\n",
    "GS=GridSearchCV(estimator=rfr,param_grid=Search_CV,n_jobs=1,cv=5)  #根据题目需求确定参数值 \n",
    "\n",
    "GS.fit(X_train,Y_train)   # 使用去除异常值后的训练集数据训练模型\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:39:36.195735Z",
     "start_time": "2020-06-16T00:39:36.191739Z"
    }
   },
   "outputs": [],
   "source": [
    "# 查看最优参数组合\n",
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:27.950412Z",
     "start_time": "2020-06-16T00:40:22.666442Z"
    }
   },
   "outputs": [],
   "source": [
    "# 用上面得到的最优参数组合重新实例化模型\n",
    "rfr=RandomForestRegressor(n_estimators=181,\n",
    "                         max_depth=9,).fit(X_train,Y_train)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:30.380572Z",
     "start_time": "2020-06-16T00:40:30.207691Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "rfr.score(X_train,Y_train),rfr.score(X_test,Y_test)   # 随机森林回归算法的score返回的是R²，并不是mse(尽管实例化时用的criterion='mse') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:48.947100Z",
     "start_time": "2020-06-16T00:40:39.499061Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# # 使用交叉验证---随机森林回归器在测试集上的MSE平均得分（5分）\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# 随机森林回归器在测试集上的MSE平均得分\n",
    "cross_val_score(rfr,X_test,Y_test,cv=5, scoring='neg_mean_squared_error').mean()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:36:54.809803Z",
     "start_time": "2020-06-16T00:36:54.803807Z"
    },
    "scrolled": false
   },
   "source": [
    "'''\n",
    "可以看出：无论是在训练集上还是测试集上，随机森林的的表现均优于LinearRegression,Lasson和Ridge：\n",
    "训练集上的MSE由原来的0.25降到了0.15，R²由原来的0.75上升到了0.85。\n",
    "'''"
   ]
  }
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